This bibliography shows the most interesting papers selected from pmc PubMedCentral which were newly included in covid013E |
1. Sports balls as potential SARS-CoV-2 transmission vectors. | *C7350886* C7350886 | ||
2. Water matrices as potential source of SARS-CoV-2 transmission – An overview from environmental perspective. | *C7347329* C7347329 | ||
3. Integrating Biomedical Ecological and Sustainability Sciences to Manage Emerging Infectious Diseases. | *C7340083* C7340083 | ||
4. Application of Geospatial Technologies in the COVID-19 Fight of Ghana. | *C7334632* C7334632 | ||
5. Health Care Workers and Patients as Trojan Horses: a COVID19 ward outbreak. | *C7334135* C7334135 | ||
6. An internet of things assisted drone based approach to reduce rapid spread of COVID-19. | *C7331559* C7331559 | ||
7. A Survey on Deep Transfer Learning to Edge Computing for Mitigating the COVID-19 Pandemic. | *C7326453* C7326453 | ||
8. ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India. | *C7321776* C7321776 | ||
9. Does weather affect the growth rate of COVID-19 a study to comprehend transmission dynamics on human health. | *C7321777* C7321777 | ||
10. Do drones have a realistic place in a pandemic fight for delivering medical supplies in healthcare systems problems? | *C7314667* C7314667 | ||
11. The role of economic structural factors in determining pandemic mortality rates: Evidence from the COVID-19 outbreak in France. | *C7309896* C7309896 | ||
12. A Predictive Model for the Evolution of COVID-19. | *C7306451* C7306451 | ||
13. Water and Pathogenic Viruses Inactivation—Food Engineering Perspectives. | *C7305474* C7305474 | ||
14. SARS-CoV-2 pandemic: An emerging public health concern for the poorest in Bangladesh. | *C7305720* C7305720 | ||
15. The Perfect Storm: How to Prepare against Climate Risk and Disaster Shocks in the Time of COVID-19. | *C7304397* C7304397 | ||
16. Tests sériques et déconfinement progressif. | *C7302758* C7302758 | ||
17. Policy responses and government science advice for the COVID 19 pandemic in the Philippines: January to April 2020. | *C7299863* C7299863 | ||
18. Brazil’s vulnerability to COVID-19 quantified by a spatial metric. | *C7301792* C7301792 | ||
19. COVID-19: The unreasonable effectiveness of simple models. | *C7297692* C7297692 | ||
20. Understanding Trend of the Covid-19 Fatalities in India. | *C7298687* C7298687 | ||
21. Experts examine MH consequences of social distancing. | *C7307024* C7307024 | ||
22. Catastrophe évolutive quelle pourrait-être l’influence des conditions météorologiques sur l’évolution de la pandémie CoViD-19? | *C7287447* C7287447 | ||
23. Peruvian efforts to contain COVID-19 fail to protect vulnerable population groups. | *C7286823* C7286823 | ||
24. Emergence of Blue Sky Over Delhi Due to Coronavirus Disease (COVID-19) Lockdown Implications. | *C7246289* C7246289 | ||
25. Some fractal thoughts about the COVID-19 infection outbreak. | *C7246023* C7246023 | ||
26. On the importance of population-based serological surveys of SARS-CoV-2 without overlooking their inherent uncertainties. | *C7242922* C7242922 | ||
27. DR CONGO: Coronavirus Challenge. | *C7273054* C7273054 | ||
28. Malawi – Covid‐19 Promise to Protect Economy. | *C7273011* C7273011 | ||
29. SENEGAL: Force‐Covid‐19. | *C7272959* C7272959 | ||
30. ZIMBABWE: UN Launches Virus Aid Plan. | *C7272954* C7272954 | ||
31. LIBYA: Virus Lockdown. | *C7272951* C7272951 | ||
32. RWANDA: Lockdown Support. | *C7272910* C7272910 | ||
33. Visualization of COVID-19 spread based on spread and extinction indexes. | *C7235974* C7235974 | ||
34. COVID-19 and importance of environmental sustainability. | *C7220565* C7220565 | ||
35. Epidemiology reveals mask wearing by the public is crucial for COVID-19 control. | *C7219391* C7219391 | ||
36. Une infection par le SARS-CoV-2 peut en cacher une autre. | *C7218392* C7218392 | ||
37. Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing☆. | *C7215145* C7215145 | ||
38. Do No Harm: Plastics are playing a major role in giving healthcare professionals the tools and capabilities they need to battle the COVID pandemic. | *C7267270* C7267270 | ||
39. Managing enduring public health emergencies such as COVID-19: lessons from Uganda Red Cross Society’s Ebola virus disease response operation. | *C7253222* C7253222 | ||
40. Testing on the move: South Korea s rapid response to the COVID-19 pandemic. | *C7172645* C7172645 | ||
41. SARS-CoV-2 pandemic in India: what might we expect? | *C7166000* C7166000 | ||
42. Epidemic Surveillance of Covid-19: Considering Uncertainty and Under-Ascertainment. | *C7206356* C7206356 | ||
43. How you can help with COVID-19 modelling. | *C7144181* C7144181 | ||
44. Review and analysis of current responses to COVID-19 in Indonesia: Period of January to March 2020☆. | *C7149002* C7149002 | ||
45. COVID-19 and the Air We Breathe. | *C7172577* C7172577 | ||
46. Pangolins and bats living together in underground burrows in Lopé National Park Gabon. | *C7323177* C7323177 | ||
47. Longer incubation period of coronavirus disease 2019 (COVID‐19) in older adults. | *C7280705* C7280705 | ||
48. The impacts of COVID-19 measures on global environment and fertility rate: double coincidence. | *C7353826* C7353826 | ||
49. Impact of lockdown on air quality in India during COVID-19 pandemic. | *C7338669* C7338669 | ||
50. Has air quality improved in Ecuador during the COVID-19 pandemic? A parametric analysis. | *C7338136* C7338136 | ||
51. Valuation of air pollution externalities: comparative assessment of economic damage and emission reduction under COVID-19 lockdown. | *C7286556* C7286556 | ||
52. Co-variance nexus between COVID-19 mortality humidity and air quality index in Wuhan China: New insights from partial and multiple wavelet coherence. | *C7279636* C7279636 | ||
53. COVID-19: air pollution remains low as people stay at home. | *C7241861* C7241861 | ||
54. Multivariate Analysis of Black Race and Environmental Temperature on COVID-19 in the US. | *C7305735* C7305735 | ||
55. The Impact of Social Vulnerability on COVID-19 in the U.S.: An Analysis of Spatially Varying Relationships. | *32703701* 32703701 | ||
56. Determinants of Taiwan’s Early Containment of COVID-19 Incidence. | *C7287555* C7287555 | ||
57. A Public Health Perspective in the Times of COVID-19. | *C7287560* C7287560 | ||
58. South Africa: Challenges and successes of the COVID-19 lockdown. | *C7250766* C7250766 | ||
59. A mathematical model to guide the re-opening of economies during the COVID-19 pandemic. | *C7351654* C7351654 | ||
60. Network-inference-based prediction of the COVID-19 epidemic outbreak in the Chinese province Hubei. | *C7341469* C7341469 | ||
61. COVID‐19 and biodiversity: The paradox of cleaner rivers and elevated extinction risk to iconic fish species. | *C7323188* C7323188 | ||
62. Desirability Optimization Models to Create the Global Healthcare Competitiveness Index. | *C7311599* C7311599 | ||
63. AI-Empowered Data Analytics for Coronavirus Epidemic Monitoring and Control. Artificial Intelligence for Coronavirus Outbreak ; (): 47-71 | *C7307709* C7307709 | ||
64. AI-Enabled Technologies that Fight the Coronavirus Outbreak. Artificial Intelligence for Coronavirus Outbreak ; (): 23-45 | *C7307708* C7307708 | ||
65. Vietnam’s Response to the COVID-19 Outbreak. | *C7348105* C7348105 | ||
66. An analysis of the ethics of lockdown in India. | *C7347396* C7347396 | ||
67. Sharing Information on COVID-19: the ethical challenges in the Malaysian setting. | *C7315401* C7315401 | ||
68. Development of a semi-structured multifaceted computer-aided questionnaire for outbreak investigation: e-Outbreak Platform. | *32654885* 32654885 | ||
69. Charting the challenges behind the testing of COVID-19 in developing countries: Nepal as a case study. | *C7219426* C7219426 | ||
70. Decoding the global outbreak of COVID-19: the nature is behind the scene. | *C7306562* C7306562 | ||
71. Statistical analysis and visualization of the potential cases of pandemic coronavirus. | *C7283982* C7283982 | ||
72. Wheat and chaffs in the interpretation of the current COVID19 outbreak in Italy. | *C7274266* C7274266 | ||
1. Sports balls as potential SARS-CoV-2 transmission vectors. | *C7350886* C7350886 | ||
2. Water matrices as potential source of SARS-CoV-2 transmission – An overview from environmental perspective. | *C7347329* C7347329 | ||
3. Integrating Biomedical Ecological and Sustainability Sciences to Manage Emerging Infectious Diseases. | *C7340083* C7340083 | ||
4. Application of Geospatial Technologies in the COVID-19 Fight of Ghana. | *C7334632* C7334632 | ||
5. Health Care Workers and Patients as Trojan Horses: a COVID19 ward outbreak. | *C7334135* C7334135 | ||
6. An internet of things assisted drone based approach to reduce rapid spread of COVID-19. | *C7331559* C7331559 | ||
7. A Survey on Deep Transfer Learning to Edge Computing for Mitigating the COVID-19 Pandemic. | *C7326453* C7326453 | ||
8. ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India. | *C7321776* C7321776 | ||
9. Does weather affect the growth rate of COVID-19 a study to comprehend transmission dynamics on human health. | *C7321777* C7321777 | ||
10. Do drones have a realistic place in a pandemic fight for delivering medical supplies in healthcare systems problems? | *C7314667* C7314667 | ||
11. The role of economic structural factors in determining pandemic mortality rates: Evidence from the COVID-19 outbreak in France. | *C7309896* C7309896 | ||
12. A Predictive Model for the Evolution of COVID-19. | *C7306451* C7306451 | ||
13. Water and Pathogenic Viruses Inactivation—Food Engineering Perspectives. | *C7305474* C7305474 | ||
14. SARS-CoV-2 pandemic: An emerging public health concern for the poorest in Bangladesh. | *C7305720* C7305720 | ||
15. The Perfect Storm: How to Prepare against Climate Risk and Disaster Shocks in the Time of COVID-19. | *C7304397* C7304397 | ||
16. Tests sériques et déconfinement progressif. | *C7302758* C7302758 | ||
17. Policy responses and government science advice for the COVID 19 pandemic in the Philippines: January to April 2020. | *C7299863* C7299863 | ||
18. Brazil’s vulnerability to COVID-19 quantified by a spatial metric. | *C7301792* C7301792 | ||
19. COVID-19: The unreasonable effectiveness of simple models. | *C7297692* C7297692 | ||
20. Understanding Trend of the Covid-19 Fatalities in India. | *C7298687* C7298687 | ||
21. Experts examine MH consequences of social distancing. | *C7307024* C7307024 | ||
22. Catastrophe évolutive quelle pourrait-être l’influence des conditions météorologiques sur l’évolution de la pandémie CoViD-19? | *C7287447* C7287447 | ||
23. Peruvian efforts to contain COVID-19 fail to protect vulnerable population groups. | *C7286823* C7286823 | ||
24. Emergence of Blue Sky Over Delhi Due to Coronavirus Disease (COVID-19) Lockdown Implications. | *C7246289* C7246289 | ||
25. Some fractal thoughts about the COVID-19 infection outbreak. | *C7246023* C7246023 | ||
26. On the importance of population-based serological surveys of SARS-CoV-2 without overlooking their inherent uncertainties. | *C7242922* C7242922 | ||
27. DR CONGO: Coronavirus Challenge. | *C7273054* C7273054 | ||
28. Malawi – Covid‐19 Promise to Protect Economy. | *C7273011* C7273011 | ||
29. SENEGAL: Force‐Covid‐19. | *C7272959* C7272959 | ||
30. ZIMBABWE: UN Launches Virus Aid Plan. | *C7272954* C7272954 | ||
31. LIBYA: Virus Lockdown. | *C7272951* C7272951 | ||
32. RWANDA: Lockdown Support. | *C7272910* C7272910 | ||
33. Visualization of COVID-19 spread based on spread and extinction indexes. | *C7235974* C7235974 | ||
34. COVID-19 and importance of environmental sustainability. | *C7220565* C7220565 | ||
35. Epidemiology reveals mask wearing by the public is crucial for COVID-19 control. | *C7219391* C7219391 | ||
36. Une infection par le SARS-CoV-2 peut en cacher une autre. | *C7218392* C7218392 | ||
37. Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing☆. | *C7215145* C7215145 | ||
38. Do No Harm: Plastics are playing a major role in giving healthcare professionals the tools and capabilities they need to battle the COVID pandemic. | *C7267270* C7267270 | ||
39. Managing enduring public health emergencies such as COVID-19: lessons from Uganda Red Cross Society’s Ebola virus disease response operation. | *C7253222* C7253222 | ||
40. Testing on the move: South Korea s rapid response to the COVID-19 pandemic. | *C7172645* C7172645 | ||
41. SARS-CoV-2 pandemic in India: what might we expect? | *C7166000* C7166000 | ||
42. Epidemic Surveillance of Covid-19: Considering Uncertainty and Under-Ascertainment. | *C7206356* C7206356 | ||
43. How you can help with COVID-19 modelling. | *C7144181* C7144181 | ||
44. Review and analysis of current responses to COVID-19 in Indonesia: Period of January to March 2020☆. | *C7149002* C7149002 | ||
45. COVID-19 and the Air We Breathe. | *C7172577* C7172577 | ||
46. Pangolins and bats living together in underground burrows in Lopé National Park Gabon. | *C7323177* C7323177 | ||
47. Longer incubation period of coronavirus disease 2019 (COVID‐19) in older adults. | *C7280705* C7280705 | ||
48. The impacts of COVID-19 measures on global environment and fertility rate: double coincidence. | *C7353826* C7353826 | ||
49. Impact of lockdown on air quality in India during COVID-19 pandemic. | *C7338669* C7338669 | ||
50. Has air quality improved in Ecuador during the COVID-19 pandemic? A parametric analysis. | *C7338136* C7338136 | ||
51. Valuation of air pollution externalities: comparative assessment of economic damage and emission reduction under COVID-19 lockdown. | *C7286556* C7286556 | ||
52. Co-variance nexus between COVID-19 mortality humidity and air quality index in Wuhan China: New insights from partial and multiple wavelet coherence. | *C7279636* C7279636 | ||
53. COVID-19: air pollution remains low as people stay at home. | *C7241861* C7241861 | ||
54. Multivariate Analysis of Black Race and Environmental Temperature on COVID-19 in the US. | *C7305735* C7305735 | ||
55. The Impact of Social Vulnerability on COVID-19 in the U.S.: An Analysis of Spatially Varying Relationships. | *32703701* 32703701 | ||
56. Determinants of Taiwan’s Early Containment of COVID-19 Incidence. | *C7287555* C7287555 | ||
57. A Public Health Perspective in the Times of COVID-19. | *C7287560* C7287560 | ||
58. South Africa: Challenges and successes of the COVID-19 lockdown. | *C7250766* C7250766 | ||
59. A mathematical model to guide the re-opening of economies during the COVID-19 pandemic. | *C7351654* C7351654 | ||
60. Network-inference-based prediction of the COVID-19 epidemic outbreak in the Chinese province Hubei. | *C7341469* C7341469 | ||
61. COVID‐19 and biodiversity: The paradox of cleaner rivers and elevated extinction risk to iconic fish species. | *C7323188* C7323188 | ||
62. Desirability Optimization Models to Create the Global Healthcare Competitiveness Index. | *C7311599* C7311599 | ||
63. AI-Empowered Data Analytics for Coronavirus Epidemic Monitoring and Control. Artificial Intelligence for Coronavirus Outbreak ; (): 47-71 | *C7307709* C7307709 | ||
64. AI-Enabled Technologies that Fight the Coronavirus Outbreak. Artificial Intelligence for Coronavirus Outbreak ; (): 23-45 | *C7307708* C7307708 | ||
65. Vietnam’s Response to the COVID-19 Outbreak. | *C7348105* C7348105 | ||
66. An analysis of the ethics of lockdown in India. | *C7347396* C7347396 | ||
67. Sharing Information on COVID-19: the ethical challenges in the Malaysian setting. | *C7315401* C7315401 | ||
68. Development of a semi-structured multifaceted computer-aided questionnaire for outbreak investigation: e-Outbreak Platform. | *32654885* 32654885 | ||
69. Charting the challenges behind the testing of COVID-19 in developing countries: Nepal as a case study. | *C7219426* C7219426 | ||
70. Decoding the global outbreak of COVID-19: the nature is behind the scene. | *C7306562* C7306562 | ||
71. Statistical analysis and visualization of the potential cases of pandemic coronavirus. | *C7283982* C7283982 | ||
72. Wheat and chaffs in the interpretation of the current COVID19 outbreak in Italy. | *C7274266* C7274266 | ||
73. Researchers Continue Quest to Contain Spread of COVID-19: Digital technologies aim to accelerate contact tracing. | *C7337627* C7337627 | ||
74. Integrating Biodiversity Infrastructure into Pathogen Discovery and Mitigation of Emerging Infectious Diseases. | *32665736* 32665736 | ||
75. Lessons from the field on COVID-19: a public health point of view. | *C7335696* C7335696 | ||
76. COVID-19 has no effect on gravity. | *C7299649* C7299649 | ||
77. Impact of intervention methods on COVID-19 transmission in Shenzhen. | *C7331564* C7331564 | ||
78. Les abattoirs : une cible majeure pour la prévention de la COVID-19. | *C7358161* C7358161 | ||
79. COVID-19: Interpretation of morbidity and mortality data. | *C7354261* C7354261 | ||
80. Slaughterhouses: A major target for COVID-19 prevention. | *C7354259* C7354259 | ||
81. COVID-19 epidemic phases: Criteria challenges and issues for the future☆. | *C7347349* C7347349 | ||
82. Approche québécoise de Santé publique face la pandémie de COVID-19. | *C7280803* C7280803 | ||
83. Que déduire des études évaluant l’effet du climat sur la COVID-19 ? | *C7280832* C7280832 | ||
84. Épidémies et environnement sont liés ! | *C7271843* C7271843 | ||
85. The use of Smartphones to monitor the deconfinement of Covid-19 in France. | *C7248626* C7248626 | ||
86. COVID-19 crisis exit phases. | *C7247800* C7247800 | ||
87. For the opening of COVID-19 hotels. | *C7241997* C7241997 | ||
88. Covid-19 tests: Collective and individual applications. | *C7241985* C7241985 | ||
89. Emerging from the COVID-19 outbreak: For a methodology of deconfinement that respects the human being. | *C7234775* C7234775 | ||
90. Modelling scenarios of the epidemic of COVID-19 in Canada. | *32673384* 32673384 | ||
91. A Silent Infection Pandemic of COVID-19: Epidemiological Investigation and Hypothetical Models. | *32670439* 32670439 | ||
92. Should Contact Bans Have Been Lifted More in Germany?: A Quantitative Prediction of Its Effects. | *C7337731* C7337731 | ||
93. Global stability and cost-effectiveness analysis of COVID-19 considering the impact of the environment:using data from Ghana. | *C7351453* C7351453 | ||
94. The first 100 days: Modeling the evolution of the COVID-19 pandemic. | *C7351399* C7351399 | ||
95. Assessment of Lockdown Effect in Some States and Overall India: A Predictive Mathematical Study on COVID-19 Outbreak. | *C7345298* C7345298 | ||
96. COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions. | *C7340090* C7340090 | ||
97. The susceptible-unidentified infected-confirmed (SUC) epidemic model for estimating unidentified infected population for COVID-19. | *C7341958* C7341958 | ||
98. Study of ARIMA and least square support vector machine (LS-SVM) models for the prediction of SARS-CoV-2 confirmed cases in the most affected countries. | *C7345281* C7345281 | ||
99. Stability analysis and numerical simulation of SEIR model for pandemic COVID-19 spread in Indonesia. | *C7345386* C7345386 | ||
100. Modeling the impact of non-pharmaceutical interventions on the dynamics of novel coronavirus with optimal control analysis with a case study. | *C7341999* C7341999 | ||
101. Statistically-based methodology for revealing real contagion trends and correcting delay-induced errors in the assessment of COVID-19 pandemic. | *C7341964* C7341964 | ||
102. Spreading of infections on random graphs: A percolation-type model for COVID-19. | *C7332959* C7332959 | ||
103. An analysis of COVID-19 spread based on fractal interpolation and fractal dimension. | *C7332948* C7332948 | ||
104. Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics. | *C7328553* C7328553 | ||
105. A novel mathematical approach of COVID-19 with non-singular fractional derivative. | *C7327473* C7327473 | ||
106. Modelling the downhill of the Sars-Cov-2 in Italy and a universal forecast of the epidemic in the world. | *C7328650* C7328650 | ||
107. A data-driven assessment of early travel restrictions related to the spreading of the novel COVID-19 within mainland China. | *C7328552* C7328552 | ||
108. An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data. | *C7324351* C7324351 | ||
109. Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables. | *C7324930* C7324930 | ||
110. Optimal Control Design of Impulsive SQEIAR Epidemic Models with Application to COVID-19. | *C7324354* C7324354 | ||
111. Influence of isolation measures for patients with mild symptoms on the spread of COVID-19. | *C7324350* C7324350 | ||
112. Analysis on novel coronavirus (COVID-19) using machine learning methods. | *C7324348* C7324348 | ||
113. A novel covid-19 mathematical model with fractional derivatives: Singular and nonsingular kernels. | *C7321058* C7321058 | ||
114. Modeling and forecasting the COVID-19 pandemic in India. | *C7321056* C7321056 | ||
115. A mathematical study on the spread of COVID-19 considering social distancing and rapid assessment: The case of Jakarta Indonesia. | *C7321057* C7321057 | ||
116. A SIR model assumption for the spread of COVID-19 in different communities. | *C7321055* C7321055 | ||
117. Epidemic in networked population with recurrent mobility pattern. | *C7315165* C7315165 | ||
118. HIV and shifting epicenters for COVID-19 an alert for some countries. | *C7316073* C7316073 | ||
119. Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19. | *C7315984* C7315984 | ||
120. Determinants of the infection rate of the COVID-19 in the U.S. using ANFIS and virus optimization algorithm (VOA). | *C7315966* C7315966 | ||
121. Forecasting COVID-19 pandemic: A data-driven analysis. | *C7315964* C7315964 | ||
122. Stationary distribution and extinction of stochastic coronavirus (COVID-19) epidemic model. | *C7311924* C7311924 | ||
123. An age-structured model for coupling within-host and between-host dynamics in environmentally-driven infectious diseases. | *C7306010* C7306010 | ||
124. Dynamic analysis of a mathematical model with health care capacity for COVID-19 pandemic. | *C7305938* C7305938 | ||
125. A data driven epidemic model to analyse the lockdown effect and predict the course of COVID-19 progress in India. | *C7305920* C7305920 | ||
126. Comprehensive identification and isolation policies have effectively suppressed the spread of COVID-19. | *C7305880* C7305880 | ||
127. Analysis of a mathematical model for COVID-19 population dynamics in Lagos Nigeria. | *C7305939* C7305939 | ||
128. A new modelling of the COVID 19 pandemic. | *C7305925* C7305925 | ||
129. Identification of influential users on Twitter: A novel weighted correlated influence measure for Covid-19. | *C7305915* C7305915 | ||
130. Quantifying the effects of quarantine using an IBM SEIR model on scalefree networks. | *C7305517* C7305517 | ||
131. Evaluation and prediction of COVID-19 in India: A case study of worst hit states. | *C7303618* C7303618 | ||
132. A hybrid multi-scale model of COVID-19 transmission dynamics to assess the potential of non-pharmaceutical interventions. | *C7269965* C7269965 | ||
133. Applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of COVID-19. | *C7269951* C7269951 | ||
134. A Python based Support Vector Regression Model for prediction of Covid19 cases in India. | *C7261465* C7261465 | ||
135. Unravelling the Myths of R0 in Controlling the Dynamics of COVID-19 Outbreak: a Modelling Perspective. | *C7261458* C7261458 | ||
136. Predicting optimal lockdown period with parametric approach using three-phase maturation SIRD model for COVID-19 pandemic. | *C7260573* C7260573 | ||
137. Modeling and prediction of COVID-19 in Mexico applying mathematical and computational models. | *C7256618* C7256618 | ||
138. Modeling COVID-19 Epidemic in Heilongjiang Province China. | *C7256610* C7256610 | ||
139. Forecasting the cumulative number of confirmed cases of COVID-19 in Italy UK and USA using fractional nonlinear grey Bernoulli model. | *C7256527* C7256527 | ||
140. Insights into the dynamics and control of COVID-19 infection rates. | *C7253979* C7253979 | ||
141. Analogies between SARS-CoV-2 infection dynamics and batch chemical reactor behavior. | *C7305736* C7305736 | ||
142. Heavy metals in submicronic particulate matter (PM1) from a Chinese metropolitan city predicted by machine learning models. | *C7340598* C7340598 | ||
143. Trend Dynamics of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Transmission in 16 Cities of Hubei Province China. | *32669878* 32669878 | ||
144. The heterogeneous age-mixing model of estimating the covid cases of different local government units in the National Capital Region Philippines. | *C7334943* C7334943 | ||
145. Modelling of reproduction number for COVID-19 in India and high incidence states. | *C7324346* C7324346 | ||
146. Forecasting COVID-19 epidemic in India and high incidence states using SIR and logistic growth models. | *C7319934* C7319934 | ||
147. When COVID-19 will decline in India? Prediction by combination of recovery and case load rate. | *C7308770* C7308770 | ||
148. COVID-19 and balance in access to health care in Ethiopia. | *C7235593* C7235593 | ||
149. On the uncertainty of real-time predictions of epidemic growths: A COVID-19 case study for China and Italy. | *C7263229* C7263229 | ||
150. Optimal control of a fractional order model for granular SEIR epidemic with uncertainty. | *C7338880* C7338880 | ||
151. Comparison of COVID-19 Pandemic Dynamics in Asian Countries with Statistical Modeling. | *32670391* 32670391 | ||
152. Non Pharmaceutical Interventions for Optimal Control of COVID-19. | *32688137* 32688137 | ||
153. COVID-19 Trends and Forecast in the Eastern Mediterranean Region With a Particular Focus on Pakistan. | *32670717* 32670717 | ||
154. Wastewater and public health: the potential of wastewater surveillance for monitoring COVID-19. | *C7291992* C7291992 | ||
155. Data-driven modelling and prediction of COVID-19 infection in India and correlation analysis of the virus transmission with socio-economic factors. | *32683321* 32683321 | ||
156. COVID-19 pandemic and its recovery time of patients in India: A pilot study. | *32673841* 32673841 | ||
157. Geographies of the COVID-19 pandemic. | *C7335941* C7335941 | ||
158. The 2019-nCoV pandemic in the global south: A Tsunami ahead. | *C7234938* C7234938 | ||
159. A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate. | *C7305924* C7305924 | ||
160. An updated min-review on environmental route of the SARS-CoV-2 transmission. | *C7346818* C7346818 | ||
161. Impact of human mobility on the transmission dynamics of infectious diseases. | *C7242095* C7242095 | ||
162. Unexpected rise of ozone in urban and rural areas and sulfur dioxide in rural areas during the coronavirus city lockdown in Hangzhou China: implications for air quality. | *C7292245* C7292245 | ||
163. Environmental chemistry is most relevant to study coronavirus pandemics. | *C7237241* C7237241 | ||
164. Coronavirus pandemic versus temperature in the context of Indian subcontinent: a preliminary statistical analysis. | *C7347760* C7347760 | ||
165. Understanding COVID-19 transmission through Bayesian probabilistic modeling and GIS-based Voronoi approach: a policy perspective. | *C7340861* C7340861 | ||
166. Coronaviruses in wastewater processes: Source fate and potential risks. | *C7346830* C7346830 | ||
167. The effect of latitude and PM2.5 on spreading of SARS-CoV-2 in tropical and temperate zone countries☆. | *32683090* 32683090 | ||
168. COVID-19 prevalence and fatality rates in association with air pollution emission concentrations and emission sources☆. | *C7320861* C7320861 | ||
169. Association between NO2 cumulative exposure and influenza prevalence in mountainous regions: A case study from southwest China. | *C7354378* C7354378 | ||
170. The mediating effect of air quality on the association between human mobility and COVID-19 infection in China. | *32678740* 32678740 | ||
171. Massive use of disinfectants against COVID-19 poses potential risks to urban wildlife. | *C7346835* C7346835 | ||
172. Short-term exposure to ambient air quality of the most polluted Indian cities due to lockdown amid SARS-CoV-2. | *C7330599* C7330599 | ||
173. Population-level COVID-19 mortality risk for non-elderly individuals overall and for non-elderly individuals without underlying diseases in pandemic epicenters. | *C7327471* C7327471 | ||
174. Increased Use of Quaternary Ammonium Compounds during the SARS-CoV-2 Pandemic and Beyond: Consideration of Environmental Implications. | *C7341688* C7341688 | ||
175. Impacts of Modifiable Factors on Ambient Air Pollution: A Case Study of COVID-19 Shutdowns. | *C7316094* C7316094 | ||
176. Systematic Review and Meta-Analysis of the Persistence and Disinfection of Human Coronaviruses and Their Viral Surrogates in Water and Wastewater. | *C7294895* C7294895 | ||
177. Presence of SARS-Coronavirus-2 RNA in Sewage and Correlation with Reported COVID-19 Prevalence in the Early Stage of the Epidemic in The Netherlands. | *C7254611* C7254611 | ||
178. Substantial Changes in Nitrate Oxide and Ozone after Excluding Meteorological Impacts during the COVID-19 Outbreak in Mainland China. | *C7241735* C7241735 | ||
179. I Just Can’t Get Enough (of Experts): The Numbers of COVID-19 and the Need for a European Approach to Testing. | *C7205547* C7205547 | ||
180. Stability analysis of leishmania epidemic model with harmonic mean type incidence rate. | *C7319490* C7319490 | ||
181. Common trends in the epidemic of Covid-19 disease. | *C7307948* C7307948 | ||
182. Epidemiologic Characteristics Transmission Chain and Risk Factors of Severe Infection of COVID-19 in Tianjin a Representative Municipality City of China. | *32671007* 32671007 | ||
183. Basics of Fused Deposition Modelling (FDM). | *C7257444* C7257444 | ||
184. COVID-19 in homeless populations: unique challenges and opportunities. | *C7319497* C7319497 | ||
185. Modelos predictivos de la epidemia de COVID-19 en España con curvas de Gompertz. | *32680658* 32680658 | ||
186. Population Genetics of SARS-CoV-2: Disentangling Effects of Sampling Bias and Infection Clusters. | *32663617* 32663617 | ||
187. A Spatio‐Temporal Analysis of the Environmental Correlates of COVID‐19 Incidence in Spain. | *C7300768* C7300768 | ||
188. The Response in Air Quality to the Reduction of Chinese Economic Activities during the COVID‐19 Outbreak. | *C7267158* C7267158 | ||
189. Impact of coronavirus outbreak on NO2 pollution assessed using TROPOMI and OMI observations. | *C7261997* C7261997 | ||
190. Kompetenznetz Public Health zu COVID-19. | *C7356085* C7356085 | ||
191. China s model to combat the COVID-19 epidemic: a public health emergency governance approach. | *C7358318* C7358318 | ||
192. Engaging the communities in Wuhan China during the COVID-19 outbreak. | *C7356108* C7356108 | ||
193. Longitudinal analyses of the relationship between development density and the COVID-19 morbidity and mortality rates: Early evidence from 1 165 metropolitan counties in the United States. | *C7315990* C7315990 | ||
194. Studying the progress of COVID-19 outbreak in India using SIRD model. Indian J Phys Proc Indian Assoc Cultiv Sci (2004) ; (): 1-17 | *C7308605* C7308605 | ||
195. Understanding Covid-19 transmission: The effect of temperature and health behavior on transmission rates. | *32674969* 32674969 | ||
196. An evaluation of COVID-19 in Italy: A data-driven modeling analysis. | *C7347309* C7347309 | ||
197. Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example. | *C7334973* C7334973 | ||
198. Bidirectional impact of imperfect mask use on reproduction number of COVID-19: A next generation matrix approach☆. | *C7334658* C7334658 | ||
199. Mathematical modeling and the transmission dynamics in predicting the Covid-19 - What next in combating the pandemic. | *C7335626* C7335626 | ||
200. Pushing past the tipping points in containment trajectories of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemics: A simple arithmetic rationale for crushing the curve instead of merely flattening it. | *C7326430* C7326430 | ||
201. On the role of governmental action and individual reaction on COVID-19 dynamics in South Africa: A mathematical modelling study. | *C7335420* C7335420 | ||
202. Calibrating Gompertz in reverse: What is your longevity-risk-adjusted global age?☆. | *C7339829* C7339829 | ||
203. Relationship between COVID-19 and weather: Case study in a tropical country. | *C7303605* C7303605 | ||
204. The effectiveness of quarantine and isolation determine the trend of the COVID-19 epidemic in the final phase of the current outbreak in China. | *32689711* 32689711 | ||
205. Clustering of Covid-19 morbidity cases in Germany. | *C7350573* C7350573 | ||
206. Simulation model of security control lane operation in the state of the COVID-19 epidemic. | *C7351408* C7351408 | ||
207. Analysis of Covid-19 and non-Covid-19 viruses including influenza viruses to determine the influence of intensive preventive measures in Japan. | *32663787* 32663787 | ||
208. COVID-19: socio-environmental challenges of Rohingya refugees in Bangladesh. | *C7292475* C7292475 | ||
209. Significant concurrent decrease in PM2.5 and NO2 concentrations in China during COVID-19 epidemic. | *C7328636* C7328636 | ||
210. Does temperature and humidity influence the spread of Covid-19?: A preliminary report. | *32670923* 32670923 | ||
211. Current perspective on pandemic of COVID-19 in the United States. | *32670917* 32670917 | ||
212. A spatial data model for urban spatial–temporal accessibility analysis. | *C7322706* C7322706 | ||
213. Environmental fate toxicity and risk management strategies of nanoplastics in the environment: Current status and future perspectives. | *C7345412* C7345412 | ||
214. Early stage COVID-19 disease dynamics in Germany: models and parameter identification. | *C7351563* C7351563 | ||
215. How the Islands of the South Pacific have remained relatively unscathed in the midst of the COVID-19 pandemic. | *32665107* 32665107 | ||
216. Night Sky Brightness Monitoring Network in Wuxi China. | *C7358754* C7358754 | ||
PMC7159851 1. Why India needs to extend the nationwide lockdown. | *32307297* 32307297 | ||
PMC7316056 2. Active Surveillance for Acute Respiratory Infections among Pediatric Long-Term Care Facility Staff. | *32593809* 32593809 | ||
PMC7298475 3. A call for precision in coronavirus disease case reporting: a crucial step more important now than ever. | *32561228* 32561228 | ||
PMC7205678 4. Mandatory Social Distancing Associated With Increased Doubling Time: An Example Using Hyperlocal Data. | *32564802* 32564802 | ||
PMC7161855 5. Human quarantine: Toward reducing infectious pressure on chimpanzees at the Tai Chimpanzee Project, Cote d'Ivoire. | *28095600* 28095600 | ||
PMC7308594 6. Opinion paper: Severe Acute Respiratory Syndrome Coronavirus 2 and domestic animals: what relation? | *32638677* 32638677 | ||
PMC7147856 7. Coronavirus pandemic and tourism: Dynamic stochastic general equilibrium modeling of infectious disease outbreak. | *32292219* 32292219 | ||
PMC7327374 8. Real-time estimation of the reproduction number of the novel coronavirus disease (COVID-19) in China in 2020 based on incidence data. | *32617309* 32617309 | ||
PMC7182770 9. Dynamics of an SEIR model with infectivity in incubation period and homestead-isolation on the susceptible. | *32341623* 32341623 | ||
PMC7296610 10. A Review of the Strategies and Studies on the Prevention and Control of the New Coronavirus in Workplaces. | *32607395* 32607395 | ||
PMC7221384 11. Modelado estadistico y matematico en la epidemia del coronavirus: Algunas consideraciones para minimizar los sesgos en los resultados. | *32410768* 32410768 | ||
PMC7332954 12. Looking at intra-hospital non COVID-19 mortality among elderly patients during COVID-19 pandemic. | *32652368* 32652368 | ||
PMC7301143 13. Mapping the Changes on Incidence, Case Fatality Rates and Recovery Proportion of COVID-19 in Afghanistan Using Geographical Information Systems. | *32620302* 32620302 | ||
PMC7243225 14. A plausible transmission mode. | *32444708* 32444708 | ||
PMC7212972 15. Pets and COVID-19. | *32394982* 32394982 | ||
PMC7341951 16. The World Goes Bats: Living Longer and Tolerating Viruses. | *32640245* 32640245 | ||
PMC7328914 17. Cluster-based dual evolution for multivariate time series: Analyzing COVID-19. | *32611104* 32611104 | ||
PMC7301891 18. Global dynamics of a fractional order model for the transmission of HIV epidemic with optimal control. | *32572309* 32572309 | ||
PMC7297698 19. Implicit Riesz wavelets based-method for solving singular fractional integro-differential equations with applications to hematopoietic stem cell modeling. | *32565621* 32565621 | ||
PMC7280153 20. A mathematical model of the evolution and spread of pathogenic coronaviruses from natural host to human host. | *32536758* 32536758 | ||
PMC7271877 21. Modeling the dynamics of viral infections in presence of latently infected cells. | *32518473* 32518473 | ||
PMC7213621 22. From severe acute respiratory syndrome-associated coronavirus to 2019 novel coronavirus outbreak: similarities in the early epidemics and prediction of future trends. | *32118641* 32118641 | ||
PMC7213624 23. Six weeks into the 2019 coronavirus disease outbreak: it is time to consider strategies to impede the emergence of new zoonotic infections. | *32097202* 32097202 | ||
PMC7340795 24. A systemic approach to assess the potential and risks of wildlife culling for infectious disease control. | *32636525* 32636525 | ||
PMC7243033 25. Extreme events and emergency scales. | *32501383* 32501383 | ||
PMC7344024 26. Visualisation of epidemiological map using an Internet of Things infectious disease surveillance platform. | *32646460* 32646460 | ||
PMC7188415 27. Clinical Distancing and Mitigation of Coronavirus Disease 2019. | *32426755* 32426755 | ||
PMC7325403 28. Coronavirus Disease 2019 (COVID-19): Forecast of an Emerging Urgency in Pakistan. | *32617220* 32617220 | ||
PMC7212976 29. The potential of wastewater-based epidemiology as surveillance and early warning of infectious disease outbreaks. | *32395676* 32395676 | ||
PMC7308174 30. Ecosystem Restoration: A Public Health Intervention. | *32572658* 32572658 | ||
PMC7189354 31. Catastrophic Risk: Waking Up to the Reality of a Pandemic? | *32350634* 32350634 | ||
PMC5115174 32. Models of Eucalypt phenology predict bat population flux. | *27891217* 27891217 | ||
PMC7276240 33. Hypothetical unfolding of a global conjoint epidemic. | *32434766* 32434766 | ||
PMC7287416 34. Retrospective search of SARS-CoV-2 in respiratory samples in Valles Occidental (Barcelona, Spain) before the first case was reported. | *32605841* 32605841 | ||
PMC7128895 35. Future perspectives of wastewater-based epidemiology: Monitoring infectious disease spread and resistance to the community level. | *32283358* 32283358 | ||
PMC7314693 36. The impact of COVID-19 as a necessary evil on air pollution in India during the lockdown. | *32634726* 32634726 | ||
PMC7255294 37. Detecting viral outbreaks in future using enhanced environmental surveillance. | *32504848* 32504848 | ||
PMC7307330 38. The Importance of Sewage Archiving in Coronavirus Epidemiology and Beyond. | *32551590* 32551590 | ||
PMC7163514 39. One health-Transdisciplinary opportunities for SETAC leadership in integrating and improving the health of people, animals, and the environment. | *27717067* 27717067 | ||
PMC7338397 40. The COVID-19 pandemic: a new challenge for syndromic surveillance. | *32614283* 32614283 | ||
PMC7266123 41. The coronavirus spread: the Italian case. | *32509495* 32509495 | ||
PMC7314927 42. Spread and Impact of COVID-19 in China: A Systematic Review and Synthesis of Predictions From Transmission-Dynamic Models. | *32626719* 32626719 | ||
PMC7314964 43. Pausing the Fight Against Malaria to Combat the COVID-19 Pandemic in Africa: Is the Future of Malaria Bleak? | *32625198* 32625198 | ||
PMC7284282 44. Special Features of Bat Microbiota Differ From Those of Terrestrial Mammals. | *32582057* 32582057 | ||
PMC7163950 45. Disease emergence and invasions. | *32313353* 32313353 | ||
PMC7330521 46. SARS-CoV-2 in Romania - analysis of the first confirmed case and evolution of the pandemic in Romania in the first three months. | *32656114* 32656114 | ||
PMC7330520 47. Coronavirus disease (COVID-19) in Morocco: situation update and proposed remedial measures. | *32656113* 32656113 | ||
PMC7330514 48. Iranian healthcare system against COVID-19. | *32656108* 32656108 | ||
PMC7295296 49. Sozialmedizin in Zeiten der Corona-Pandemie. | *32455464* 32455464 | ||
PMC7295304 50. Die Pandemiekatastrophe aus katastrophenrechtlicher und -medizinischer Sicht. | *32356300* 32356300 | ||
PMC7300906 51. Coronavirus disease 2019-The principles of the curve, explained simply. | *32476218* 32476218 | ||
PMC7349466 52. How Shenzhen, China avoided widespread community transmission: a potential model for successful prevention and control of COVID-19. | *32650840* 32650840 | ||
PMC7348130 53. Transmissibility of COVID-19 in 11 major cities in China and its association with temperature and humidity in Beijing, Shanghai, Guangzhou, and Chengdu. | *32650838* 32650838 | ||
PMC7308762 54. Modelling the Measles Outbreak at Hong Kong International Airport in 2019: A Data-Driven Analysis on the Effects of Timely Reporting and Public Awareness. | *32606834* 32606834 | ||
PMC7327472 55. Assessing the evolutionary persistence of ecological relationships: A review and preview. | *32622083* 32622083 | ||
PMC7228879 56. Editorial: Early Warning Systems for Pandemics: Lessons Learned from Natural Hazards. | *32427164* 32427164 | ||
PMC7312520 57. A One Health Perspective on the Human-Companion Animal Relationship with Emphasis on Zoonotic Aspects. | *32471058* 32471058 | ||
PMC7344860 58. Prediction of the Number of Patients Infected with COVID-19 Based on Rolling Grey Verhulst Models. | *32630565* 32630565 | ||
PMC7344859 59. Predicting the Epidemiological Outbreak of the Coronavirus Disease 2019 (COVID-19) in Saudi Arabia. | *32630363* 32630363 | ||
PMC7345270 60. Environments, Behaviors, and Inequalities: Reflecting on the Impacts of the Influenza and Coronavirus Pandemics in the United States. | *32580429* 32580429 | ||
PMC7246727 61. A Model for the Spread of Infectious Diseases in a Region. | *32365775* 32365775 | ||
PMC7351642 62. COVID-19 in Yemen: a crisis within crises. | *32652995* 32652995 | ||
PMC7203045 63. Implications of respiratory pathogen transmission dynamics on prevention and testing. | *32403019* 32403019 | ||
PMC7331537 64. Response to Gal Almogy: Superspreaders do matter. | *32623085* 32623085 | ||
PMC7297431 65. Preventing the Emergence of Corona Virus Disease 2019 Outbreak in Mass Gatherings: Appeal to Public Health Authorities. | *32577174* 32577174 | ||
PMC7297413 66. Exploring the Presence of Animal Origin in the Causation of Corona Virus Disease 2019 Outbreak and Strategies to Prevent Acquisition of the Infection. | *32577172* 32577172 | ||
PMC7306722 67. Global lockdown, pollution, and respiratory allergic diseases: Are we in or are we out? | *32605856* 32605856 | ||
PMC7141459 68. The sub-specialty of foot and ankle is evolving fast. | *32292258* 32292258 | ||
PMC7323934 69. The role of children in transmission of SARS-CoV-2: A rapid review. | *32612817* 32612817 | ||
PMC7321013 70. Nicaragua's surprising response to COVID-19. | *32612814* 32612814 | ||
PMC7214288 71. High time for an efficient and effective internationally-supported Zoonosis Surveillance System? | *32407759* 32407759 | ||
PMC7118625 72. Epidemiological characteristics and transmission model of Corona Virus Disease 2019 in China. | *32171870* 32171870 | ||
PMC7311919 73. Incubation period of the coronavirus disease 2019 (COVID-19) in Busan, South Korea. | *32631735* 32631735 | ||
PMC7313887 74. Research on COVID-19 based on ARIMA model(Delta)-Taking Hubei, China as an example to see the epidemic in Italy. | *32624404* 32624404 | ||
PMC7267327 75. Codon bias analysis may be insufficient for identifying host(s) of a novel virus. | *32379350* 32379350 | ||
PMC7315156 76. SARS-CoV-2 host diversity: An update of natural infections and experimental evidence. | *32624360* 32624360 | ||
PMC7323087 77. To what extent do children transmit SARS-CoV-2 virus? | *32542872* 32542872 | ||
PMC7347758 78. A Look at the First Quarantined Community in the USA: Response of Religious Communal Organizations and Implications for Public Health During the COVID-19 Pandemic. | *32651728* 32651728 | ||
PMC7330413 79. Forecasting of COVID-19: transmission models and beyond. | *32642080* 32642080 | ||
PMC7134710 80. Research needed to prevent MERS coronavirus outbreaks. | *28422017* 28422017 | ||
PMC7137148 81. Predicting pandemics. | *27998516* 27998516 | ||
PMC7270775 82. Improving epidemic surveillance and response: big data is dead, long live big data. | *32518898* 32518898 | ||
PMC7252143 83. On the fallibility of simulation models in informing pandemic responses. | *32359419* 32359419 | ||
PMC7320971 84. Estimation of the net reproductive number of COVID-19 in Iran. | *32617273* 32617273 | ||
PMC7278226 85. Lehren aus einer Kreuzfahrt mit SARS-CoV-2. | *32514985* 32514985 | ||
PMC7278248 86. Konnen Haustiere Corona auf ihre Besitzer ubertragen? : SARS-CoV-2 bei Hunden und Katzen. | *32514981* 32514981 | ||
PMC7220654 87. Research on the urban resilience evaluation with hybrid multiple attribute TOPSIS method: an example in China. | *32412523* 32412523 | ||
PMC7314335 88. Simulation: Keeping Pace With Pandemics. | *32541374* 32541374 | ||
PMC7319030 89. Modelling fatality curves of COVID-19 and the effectiveness of intervention strategies. | *32612894* 32612894 | ||
PMC7329111 90. Pre-outbreak determinants of perceived risks of corona infection and preventive measures taken. A prospective population-based study. | *32609763* 32609763 | ||
PMC7322079 91. Opinion: Intercepting pandemics through genomics. | *32493752* 32493752 | ||
PMC7237906 92. Coronavirus disease 2019: emerging lessons from the pandemic. | *32532462* 32532462 | ||
PMC7275191 93. Coronavirus disease 2019: Affordable alternatives of household disinfectants for community. | *32554174* 32554174 | ||
PMC7303625 94. COVID-19 in South Africa: lockdown strategy and its effects on public health and other contagious diseases. | *32629201* 32629201 | ||
PMC7252081 95. Health and economic consequences of applying the United States' PM2.5 automobile emission standards to other nations: a case study of France and Italy. | *32445933* 32445933 | ||
PMC7328541 96. An approximation-based approach for periodic estimation of effective reproduction number: a tool for decision-making in the context of coronavirus disease 2019 (COVID-19) outbreak. | *32653628* 32653628 | ||
PMC7168096 97. Development of a comprehensive infection control program for a short-term shelter serving trafficked women. | *30298575* 30298575 | ||
PMC7267780 98. Away-from-home food during coronavirus pandemic. | *32321617* 32321617 | ||
PMC6850612 99. A Multicompartment SIS Stochastic Model with Zonal Ventilation for the Spread of Nosocomial Infections: Detection, Outbreak Management, and Infection Control. | *30925211* 30925211 | ||
PMC7169223 100. Development of a dose-response model for SARS coronavirus. | *20497390* 20497390 | ||
PMC7321656 101. Air pollution in Ontario, Canada during the COVID-19 State of Emergency. | *32629257* 32629257 | ||
PMC7320712 102. COVID-19 and frequent use of hand sanitizers; human health and environmental hazards by exposure pathways. | *32623176* 32623176 | ||
PMC7320672 103. COVID-19: Lessons for the climate change emergency. | *32619845* 32619845 | ||
PMC7320254 104. Can PM2.5 pollution worsen the death rate due to COVID-19 in India and Pakistan? | *32615374* 32615374 | ||
PMC7319646 105. SARS-CoV-2 RNA detection in the air and on surfaces in the COVID-19 ward of a hospital in Milan, Italy. | *32619843* 32619843 | ||
PMC7319639 106. Food waste management during the COVID-19 outbreak: a holistic climate, economic and nutritional approach. | *32619842* 32619842 | ||
PMC7314691 107. Air quality changes in New York City during the COVID-19 pandemic. | *32640401* 32640401 | ||
PMC7311891 108. SARS-CoV-2 from faeces to wastewater treatment: What do we know? A review. | *32649988* 32649988 | ||
PMC7340013 109. Minimization of spreading of SARS-CoV-2 via household waste produced by subjects affected by COVID-19 or in quarantine. | *32653701* 32653701 | ||
PMC7336916 110. Impact of quarantine measures on chemical compositions of PM2.5 during the COVID-19 epidemic in Shanghai, China. | *32653718* 32653718 | ||
PMC7332911 111. Transmission of SARS-CoV-2 via fecal-oral and aerosols-borne routes: Environmental dynamics and implications for wastewater management in underprivileged societies. | *32652357* 32652357 | ||
PMC7114516 112. A review of studies on animal reservoirs of the SARS coronavirus. | *17451830* 17451830 | ||
217. The Short-run and Long-run Effects of Covid-19 on Energy and the Environment. | *C7305502* C7305502 | ||
218. The Impact of COVID-19-Related Measures on the Solar Resource in Areas with High Levels of Air Pollution. | *C7303630* C7303630 | ||
219. Understanding preventing and stopping epidemics. | *C7138414* C7138414 | ||
220. Applications of digital technology in COVID-19 pandemic planning and response. | *C7324092* C7324092 | ||
221. The effects of physical distancing on population mobility during the COVID-19 pandemic in the UK. | *C7292602* C7292602 | ||
222. Anthroponotic risk of SARS-CoV-2 precautionary mitigation and outbreak management. | *C7332268* C7332268 | ||
223. Emerging zoonotic diseases originating in mammals: a systematic review of effects of anthropogenic land‐use change. | *C7300897* C7300897 | ||
224. Ecotoxic response of nematodes to ivermectin a potential anti-COVID-19 drug treatment. | *32658716* 32658716 | ||
225. Can volcanic trace elements facilitate Covid-19 diffusion? A hypothesis stemming from the Mount Etna area Sicily. | *C7320851* C7320851 | ||
226. A comparative analysis of control measures on-board ship against COVID-19 and similar novel viral respiratory disease outbreak: Quarantine ship or disembark suspects? | *C7326407* C7326407 | ||
227. Predictive models of COVID-19 in India: A rapid review. | *C7298493* C7298493 | ||
228. Projections for novel coronavirus (COVID-19) and evaluation of epidemic response strategies for India. | *C7250784* C7250784 | ||
229. Significance of geographical factors to the COVID-19 outbreak in India. | *C7299143* C7299143 | ||
230. Enhanced secondary pollution offset reduction of primary emissions during COVID-19 lockdown in China. | *C7337733* C7337733 | ||
231. Complete dimensional collapse in the continuum limit of a delayed SEIQR network model with separable distributed infectivity. | *C7352098* C7352098 | ||
232. Computational analysis of the SARS-CoV-2 and other viruses based on the Kolmogorov’s complexity and Shannon’s information theories. | *C7335223* C7335223 | ||
233. Investigating time strength and duration of measures in controlling the spread of COVID-19 using a networked meta-population model. | *C7320847* C7320847 | ||
234. Are official confirmed cases and fatalities counts good enough to study the COVID-19 pandemic dynamics? A critical assessment through the case of Italy. | *C7319224* C7319224 | ||
235. Dynamics and control of COVID-19 pandemic with nonlinear incidence rates. | *C7315126* C7315126 | ||
236. A fractional-order model for the novel coronavirus (COVID-19) outbreak. | *C7314430* C7314430 | ||
237. Transmission dynamics of COVID-19 in Wuhan China: effects of lockdown and medical resources. | *C7313654* C7313654 | ||
238. The effect of behavior of wearing masks on epidemic dynamics. | *C7307808* C7307808 | ||
239. SEIR modeling of the COVID-19 and its dynamics. | *C7301771* C7301771 | ||
240. Prediction of bifurcations by varying critical parameters of COVID-19. | *C7298931* C7298931 | ||
241. A new SAIR model on complex networks for analysing the 2019 novel coronavirus (COVID-19). | *C7299147* C7299147 | ||
242. The impact of asymptomatic individuals on the strength of public health interventions to prevent the second outbreak of COVID-19. | *C7293829* C7293829 | ||
243. Importance of the One Health approach to study the SARS-CoV-2 in Latin America. | *C7315149* C7315149 | ||
244. The state of One Health research across disciplines and sectors – a bibliometric analysis. | *C7274982* C7274982 | ||
245. A cluster of COVID-19 infections indicating person-to-person transmission among casual contacts from social gatherings: An outbreak case-contact investigation. | *C7313868* C7313868 | ||
246. An early test and treat strategy for SARS-CoV-2. | *C7313828* C7313828 | ||
247. Spread of COVID-19 from Wuhan to rural villages in the Hubei Province. | *C7313863* C7313863 | ||
248. Modelling insights into the COVID-19 pandemic. | *32680824* 32680824 | ||
249. Novel semi-metrics for multivariate change point analysis and anomaly detection. | *C7329734* C7329734 | ||
250. Analytic solution of the SEIR epidemic model via asymptotic approximant. | *C7316071* C7316071 | ||
251. Size and timescale of epidemics in the SIR framework. | *C7305940* C7305940 | ||
252. A four-compartment model for the COVID-19 infection—implications on infection kinetics control measures and lockdown exit strategies. | *C7313816* C7313816 | ||
253. SARS-CoV-2 in sewer systems and connected facilities. | *C7334965* C7334965 | ||
254. Network of networks: preliminary lessons from the Antwerp Port Authority on crisis management and network governance to deal with the COVID‐19 pandemic. | *C7300886* C7300886 | ||
255. Fighting Against COVID‐19 through Government Initiatives and Collaborative Governance: Taiwan Experience. | *C7280728* C7280728 | ||
256. Balancing governance capacity and legitimacy ‐ how the Norwegian government handled the COVID‐19 crisis as a high performer. | *C7280699* C7280699 | ||
257. Australian Quarantine Policy: From Centralization to Coordination with mid‐Pandemic COVID‐19 Shifts. | *C7276748* C7276748 | ||
258. Human Mobility and COVID-19: A Negative Binomial Regression Analysis. | *C7351378* C7351378 | ||
259. Factors influencing health behaviours during the coronavirus disease 2019 outbreak in China: an extended information-motivation-behaviour skills model. | *C7346793* C7346793 | ||
260. Coronavirus disease 2019 mortality: a multivariate ecological analysis in relation to ethnicity population density obesity deprivation and pollution. | *32693249* 32693249 | ||
261. Modeling the effect of area deprivation on COVID-19 incidences: a study of Chennai megacity India. | *32707468* 32707468 | ||
262. First COVID-19 case in the Rohingya camp in Bangladesh: Needs proper attention. | *C7247472* C7247472 | ||
263. Correlation between PCR Examination Rate among the Population and the Containment of Pandemic of COVID-19. | *C7245269* C7245269 | ||
264. The relationship between fever rate and telework implementation as a social distancing measure against the COVID-19 pandemic in Japan. | *C7242969* C7242969 | ||
265. Prevention measures for COVID-19 in retail food stores in Braga Portugal. | *32690466* 32690466 | ||
266. Population implications of cessation of IVF during the COVID-19 pandemic. | *C7336906* C7336906 | ||
267. Evolution of effective serial interval of SARS-CoV-2 by non-pharmaceutical interventions. | *32702717* 32702717 | ||
268. The potential exposure and transmission risk of SARS-CoV-2 through sludge treatment and disposal. | *C7340072* C7340072 | ||
269. Resources and Waste Management in COVID-19 and Pandemics. | *C7308225* C7308225 | ||
270. Der Gasteiner Heilstollen und eine mögliche Ansteckungsgefahr im Therapiebereich mit Viren. | *C7309198* C7309198 | ||
271. Some respite for India s dirtiest river? Examining the Yamuna s water quality at Delhi during the COVID-19 lockdown period. | *C7358175* C7358175 | ||
272. Reductions in traffic-related black carbon and ultrafine particle number concentrations in an urban neighborhood during the COVID-19 pandemic. | *C7358174* C7358174 | ||
273. Presence and infectivity of SARS-CoV-2 virus in wastewaters and rivers. | *32693284* 32693284 | ||
274. Spatial and temporal differentiation of COVID-19 epidemic spread in mainland China and its influencing factors. | *32687995* 32687995 | ||
275. Containing the spread of coronavirus disease 2019 (COVID-19): Meteorological factors and control strategies. | *32688005* 32688005 | ||
276. Response of major air pollutants to COVID-19 lockdowns in China. | *C7351666* C7351666 | ||
277. Meteorological impact on the COVID-19 pandemic: A study across eight severely affected regions in South America. | *32674022* 32674022 | ||
278. Levels and sources of hourly PM2.5-related elements during the control period of the COVID-19 pandemic at a rural site between Beijing and Tianjin. | *32674021* 32674021 | ||
279. SARS-CoV-2 in river water: Implications in low sanitation countries. | *32679506* 32679506 | ||
280. A chemical cocktail during the COVID-19 outbreak in Beijing China: Insights from six-year aerosol particle composition measurements during the Chinese New Year holiday. | *C7334657* C7334657 | ||
281. Wastewater surveillance for Covid-19: An African perspective. | *32659559* 32659559 | ||
282. Repercussions of COVID-19 pandemic on municipal solid waste management: Challenges and opportunities. | *32663690* 32663690 | ||
283. Preprocessing alternatives for compositional data related to water sanitation and hygiene. | *32663686* 32663686 | ||
284. A vulnerability-based approach to human-mobility reduction for countering COVID-19 transmission in London while considering local air quality. | *C7315141* C7315141 | ||
285. SARS-CoV-2 pandemic lockdown: Effects on air quality in the industrialized Gujarat state of India. | *C7305892* C7305892 | ||
286. First environmental surveillance for the presence of SARS-CoV-2 RNA in wastewater and river water in Japan. | *C7305903* C7305903 | ||
287. Satellite-detected tropospheric nitrogen dioxide and spread of SARS-CoV-2 infection in Northern Italy. | *C7297152* C7297152 | ||
217. The Short-run and Long-run Effects of Covid-19 on Energy and the Environment. | *C7305502* C7305502 | ||
218. The Impact of COVID-19-Related Measures on the Solar Resource in Areas with High Levels of Air Pollution. | *C7303630* C7303630 | ||
219. Understanding preventing and stopping epidemics. | *C7138414* C7138414 | ||
220. Applications of digital technology in COVID-19 pandemic planning and response. | *C7324092* C7324092 | ||
221. The effects of physical distancing on population mobility during the COVID-19 pandemic in the UK. | *C7292602* C7292602 | ||
222. Anthroponotic risk of SARS-CoV-2 precautionary mitigation and outbreak management. | *C7332268* C7332268 | ||
223. Emerging zoonotic diseases originating in mammals: a systematic review of effects of anthropogenic land‐use change. | *C7300897* C7300897 | ||
224. Ecotoxic response of nematodes to ivermectin a potential anti-COVID-19 drug treatment. | *32658716* 32658716 | ||
225. Can volcanic trace elements facilitate Covid-19 diffusion? A hypothesis stemming from the Mount Etna area Sicily. | *C7320851* C7320851 | ||
226. A comparative analysis of control measures on-board ship against COVID-19 and similar novel viral respiratory disease outbreak: Quarantine ship or disembark suspects? | *C7326407* C7326407 | ||
227. Predictive models of COVID-19 in India: A rapid review. | *C7298493* C7298493 | ||
228. Projections for novel coronavirus (COVID-19) and evaluation of epidemic response strategies for India. | *C7250784* C7250784 | ||
229. Significance of geographical factors to the COVID-19 outbreak in India. | *C7299143* C7299143 | ||
230. Enhanced secondary pollution offset reduction of primary emissions during COVID-19 lockdown in China. | *C7337733* C7337733 | ||
231. Complete dimensional collapse in the continuum limit of a delayed SEIQR network model with separable distributed infectivity. | *C7352098* C7352098 | ||
232. Computational analysis of the SARS-CoV-2 and other viruses based on the Kolmogorov’s complexity and Shannon’s information theories. | *C7335223* C7335223 | ||
233. Investigating time strength and duration of measures in controlling the spread of COVID-19 using a networked meta-population model. | *C7320847* C7320847 | ||
234. Are official confirmed cases and fatalities counts good enough to study the COVID-19 pandemic dynamics? A critical assessment through the case of Italy. | *C7319224* C7319224 | ||
235. Dynamics and control of COVID-19 pandemic with nonlinear incidence rates. | *C7315126* C7315126 | ||
236. A fractional-order model for the novel coronavirus (COVID-19) outbreak. | *C7314430* C7314430 | ||
237. Transmission dynamics of COVID-19 in Wuhan China: effects of lockdown and medical resources. | *C7313654* C7313654 | ||
238. The effect of behavior of wearing masks on epidemic dynamics. | *C7307808* C7307808 | ||
239. SEIR modeling of the COVID-19 and its dynamics. | *C7301771* C7301771 | ||
240. Prediction of bifurcations by varying critical parameters of COVID-19. | *C7298931* C7298931 | ||
241. A new SAIR model on complex networks for analysing the 2019 novel coronavirus (COVID-19). | *C7299147* C7299147 | ||
242. The impact of asymptomatic individuals on the strength of public health interventions to prevent the second outbreak of COVID-19. | *C7293829* C7293829 | ||
243. Importance of the One Health approach to study the SARS-CoV-2 in Latin America. | *C7315149* C7315149 | ||
244. The state of One Health research across disciplines and sectors – a bibliometric analysis. | *C7274982* C7274982 | ||
245. A cluster of COVID-19 infections indicating person-to-person transmission among casual contacts from social gatherings: An outbreak case-contact investigation. | *C7313868* C7313868 | ||
246. An early test and treat strategy for SARS-CoV-2. | *C7313828* C7313828 | ||
247. Spread of COVID-19 from Wuhan to rural villages in the Hubei Province. | *C7313863* C7313863 | ||
248. Modelling insights into the COVID-19 pandemic. | *32680824* 32680824 | ||
249. Novel semi-metrics for multivariate change point analysis and anomaly detection. | *C7329734* C7329734 | ||
250. Analytic solution of the SEIR epidemic model via asymptotic approximant. | *C7316071* C7316071 | ||
251. Size and timescale of epidemics in the SIR framework. | *C7305940* C7305940 | ||
252. A four-compartment model for the COVID-19 infection—implications on infection kinetics control measures and lockdown exit strategies. | *C7313816* C7313816 | ||
253. SARS-CoV-2 in sewer systems and connected facilities. | *C7334965* C7334965 | ||
254. Network of networks: preliminary lessons from the Antwerp Port Authority on crisis management and network governance to deal with the COVID‐19 pandemic. | *C7300886* C7300886 | ||
255. Fighting Against COVID‐19 through Government Initiatives and Collaborative Governance: Taiwan Experience. | *C7280728* C7280728 | ||
256. Balancing governance capacity and legitimacy ‐ how the Norwegian government handled the COVID‐19 crisis as a high performer. | *C7280699* C7280699 | ||
257. Australian Quarantine Policy: From Centralization to Coordination with mid‐Pandemic COVID‐19 Shifts. | *C7276748* C7276748 | ||
258. Human Mobility and COVID-19: A Negative Binomial Regression Analysis. | *C7351378* C7351378 | ||
259. Factors influencing health behaviours during the coronavirus disease 2019 outbreak in China: an extended information-motivation-behaviour skills model. | *C7346793* C7346793 | ||
260. Coronavirus disease 2019 mortality: a multivariate ecological analysis in relation to ethnicity population density obesity deprivation and pollution. | *32693249* 32693249 | ||
261. Modeling the effect of area deprivation on COVID-19 incidences: a study of Chennai megacity India. | *32707468* 32707468 | ||
262. First COVID-19 case in the Rohingya camp in Bangladesh: Needs proper attention. | *C7247472* C7247472 | ||
263. Correlation between PCR Examination Rate among the Population and the Containment of Pandemic of COVID-19. | *C7245269* C7245269 | ||
264. The relationship between fever rate and telework implementation as a social distancing measure against the COVID-19 pandemic in Japan. | *C7242969* C7242969 | ||
265. Prevention measures for COVID-19 in retail food stores in Braga Portugal. | *32690466* 32690466 | ||
266. Population implications of cessation of IVF during the COVID-19 pandemic. | *C7336906* C7336906 | ||
267. Evolution of effective serial interval of SARS-CoV-2 by non-pharmaceutical interventions. | *32702717* 32702717 | ||
268. The potential exposure and transmission risk of SARS-CoV-2 through sludge treatment and disposal. | *C7340072* C7340072 | ||
269. Resources and Waste Management in COVID-19 and Pandemics. | *C7308225* C7308225 | ||
270. Der Gasteiner Heilstollen und eine mögliche Ansteckungsgefahr im Therapiebereich mit Viren. | *C7309198* C7309198 | ||
271. Some respite for India s dirtiest river? Examining the Yamuna s water quality at Delhi during the COVID-19 lockdown period. | *C7358175* C7358175 | ||
272. Reductions in traffic-related black carbon and ultrafine particle number concentrations in an urban neighborhood during the COVID-19 pandemic. | *C7358174* C7358174 | ||
273. Presence and infectivity of SARS-CoV-2 virus in wastewaters and rivers. | *32693284* 32693284 | ||
274. Spatial and temporal differentiation of COVID-19 epidemic spread in mainland China and its influencing factors. | *32687995* 32687995 | ||
275. Containing the spread of coronavirus disease 2019 (COVID-19): Meteorological factors and control strategies. | *32688005* 32688005 | ||
276. Response of major air pollutants to COVID-19 lockdowns in China. | *C7351666* C7351666 | ||
277. Meteorological impact on the COVID-19 pandemic: A study across eight severely affected regions in South America. | *32674022* 32674022 | ||
278. Levels and sources of hourly PM2.5-related elements during the control period of the COVID-19 pandemic at a rural site between Beijing and Tianjin. | *32674021* 32674021 | ||
279. SARS-CoV-2 in river water: Implications in low sanitation countries. | *32679506* 32679506 | ||
280. A chemical cocktail during the COVID-19 outbreak in Beijing China: Insights from six-year aerosol particle composition measurements during the Chinese New Year holiday. | *C7334657* C7334657 | ||
281. Wastewater surveillance for Covid-19: An African perspective. | *32659559* 32659559 | ||
282. Repercussions of COVID-19 pandemic on municipal solid waste management: Challenges and opportunities. | *32663690* 32663690 | ||
283. Preprocessing alternatives for compositional data related to water sanitation and hygiene. | *32663686* 32663686 | ||
284. A vulnerability-based approach to human-mobility reduction for countering COVID-19 transmission in London while considering local air quality. | *C7315141* C7315141 | ||
285. SARS-CoV-2 pandemic lockdown: Effects on air quality in the industrialized Gujarat state of India. | *C7305892* C7305892 | ||
286. First environmental surveillance for the presence of SARS-CoV-2 RNA in wastewater and river water in Japan. | *C7305903* C7305903 | ||
287. Satellite-detected tropospheric nitrogen dioxide and spread of SARS-CoV-2 infection in Northern Italy. | *C7297152* C7297152 | ||
289. Comparison of virus concentration methods for the RT-qPCR-based recovery of murine hepatitis virus a surrogate for SARS-CoV-2 from untreated wastewater. | *C7273154* C7273154 | ||
290. Scenario-driven forecasting: modeling peaks and paths. Insights from the COVID-19 pandemic in Belgium. | *C7355133* C7355133 | ||
291. The dynamic effects of infectious disease outbreaks: The case of pandemic influenza and human coronavirus. | *C7286241* C7286241 | ||
292. Daily surveillance of COVID-19 using the prospective space-time scan statistic in the United States. | *C7320856* C7320856 | ||
293. Spatial analysis of COVID-19 clusters and contextual factors in New York City. | *C7306208* C7306208 | ||
294. COVID-19 and water. | *C7346856* C7346856 | ||
295. Meteorological impacts on the incidence of COVID-19 in the U.S. | *C7334896* C7334896 | ||
296. Undersampling in action and at scale: application to the COVID-19 pandemic. | *C7265167* C7265167 | ||
297. A machine learning forecasting model for COVID-19 pandemic in India. | *C7261047* C7261047 | ||
298. Temporary reduction in fine particulate matter due to ‘anthropogenic emissions switch-off’ during COVID-19 lockdown in Indian cities. | *C7357527* C7357527 | ||
299. Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices. | *C7333601* C7333601 | ||
300. The contribution of dry indoor built environment on the spread of Coronavirus: Data from various Indian states. | *C7329687* C7329687 | ||
301. Antivirus-built environment: Lessons learned from Covid-19 pandemic. | *C7313520* C7313520 | ||
302. Spatial distribution characteristics of PM2.5 and PM10 in Xi’an City predicted by land use regression models. | *C7293537* C7293537 | ||
303. Spatially‐explicit models for exploring COVID‐19 lockdown strategies. | *C7283721* C7283721 | ||
304. Gaussian approach for probability and correlation between the number of COVID-19 cases and the air pollution in Lima. | *C7332952* C7332952 | ||
305. Coronaviruses in cats and other companion animals: Where does SARS-CoV-2/COVID-19 fit? | *C7309752* C7309752 | ||
306. Making waves: Wastewater surveillance of SARS-CoV-2 for population-based health management. | *32707307* 32707307 | ||
307. Metagenomic water quality monitoring with a portable laboratory. | *32688150* 32688150 | ||
308. Identification of multiple potential viral diseases in a large urban center using wastewater surveillance. | *C7342010* C7342010 | ||
309. What settings have been linked to SARS-CoV-2 transmission clusters? | *C7327724* C7327724 | ||
310. Räumliche Ausbreitung von COVID-19 durch interregionale Verflechtungen. | *C7335932* C7335932 | ||
311. Fighting COVID-19 in Hong Kong: The effects of community and social mobilization. | *C7315977* C7315977 | ||
PMC7307990 113. Effect of the social distancing measures on the spread of COVID-19 in 10 highly infected countries. | *32623158* 32623158 | ||
PMC7303629 114. Detection of SARS-CoV-2 RNA residue on object surfaces in nucleic acid testing laboratory using droplet digital PCR. | *32619841* 32619841 | ||
PMC7291978 115. Wuhan COVID-19 data - An example to show the importance of public health interventions to fight against the pandemic. | *32540478* 32540478 | ||
PMC7228320 116. Circulation of pantropic canine coronavirus in autochthonous and imported dogs, Italy. | *32163663* 32163663 | ||
PMC7228218 117. Surveillance and taxonomic analysis of the coronavirus dominant in pigeons in China. | *32163661* 32163661 | ||
PMC7168564 118. What do studies in wild mammals tell us about human emerging viral diseases in Mexico? | *31461573* 31461573 | ||
PMC5086320 119. Bat Hunting and Bat-Human Interactions in Bangladeshi Villages: Implications for Zoonotic Disease Transmission and Bat Conservation. | *27125493* 27125493 | ||
PMC7169817 120. Detection of Severe Acute Respiratory Syndrome-Like, Middle East Respiratory Syndrome-Like Bat Coronaviruses and Group H Rotavirus in Faeces of Korean Bats. | *27213718* 27213718 | ||
PMC7138189 121. Preliminary epidemiological analysis of suspected cases of corona virus infection in Libya. | *32205266* 32205266 | ||
PMC7314681 122. Geographies of risk: Emerging infectious diseases and travel health data. | *32592905* 32592905 | ||
PMC7347475 123. Controlling the COVID-19 pandemic: Useful lessons from Vietnam. | *32653477* 32653477 | ||
PMC7191295 124. COVID-19's final frontier: The central Africa region. | *32360410* 32360410 | ||
PMC7195138 125. Senegal faces the coronavirus disease -19 challenge. | *32334087* 32334087 | ||
PMC7129830 126. Corona virus infection in Syria, Libya and Yemen; an alarming devastating threat. | *32247929* 32247929 | ||
PMC3509683 127. Human coronaviruses: insights into environmental resistance and its influence on the development of new antiseptic strategies. | *23202515* 23202515 | ||
PMC7211501 128. Viral indicators for tracking domestic wastewater contamination in the aquatic environment. | *32417460* 32417460 | ||
PMC7317462.2 129. Black, Asian and Minority Ethnic groups in England are at increased risk of death from COVID-19: indirect standardisation of NHS mortality data. | *32613083* 32613083 | ||
PMC7271642 130. Ausser Kontrolle: Regionale Einflusse auf Gesundheit und Ergebnisse. | *32518607* 32518607 | ||
PMC7151375 131. Climbing Gyms as Possible High-Risk Transmission Locations in Microbial Outbreaks. | *32493665* 32493665 | ||