The role of weather conditions in COVID-19 transmission: A study of a global panel of 1236 regions
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Article activity feed
-
-
-
-
SciScore for 10.1101/2020.07.29.20164152: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on page 27. At …
SciScore for 10.1101/2020.07.29.20164152: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on page 27. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
-
SciScore for 10.1101/2020.07.29.20164152: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources Met hod4.1Stat ist icalAnalysisW eb uiltamultiv ari ate regressionm odel(s eeE q.(1))toexp lorethew ea therc o nditioneffec tontransmi ssion :,=( ,−Δ ) +,− Δ+ ,−Δ++++ ++ℎ ++(1),= (,,−,−1)/ 2(2)0, ,= 1+,+ℎ(1−ℎ)(,) (3), = (,)/(4)wherei i n dexesare g ion,taday,andΔ a lagday. ,−Δ ) + ,−Δ + ,−Δ + + + + + + ℎ + +suggested: NoneResults from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data …
SciScore for 10.1101/2020.07.29.20164152: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources Met hod4.1Stat ist icalAnalysisW eb uiltamultiv ari ate regressionm odel(s eeE q.(1))toexp lorethew ea therc o nditioneffec tontransmi ssion :,=( ,−Δ ) +,− Δ+ ,−Δ++++ ++ℎ ++(1),= (,,−,−1)/ 2(2)0, ,= 1+,+ℎ(1−ℎ)(,) (3), = (,)/(4)wherei i n dexesare g ion,taday,andΔ a lagday. ,−Δ ) + ,−Δ + ,−Δ + + + + + + ℎ + +suggested: NoneResults from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
Some limitations of the present study should also be pointed out. First, our conclusions were drawn based on observations over certain periods, thus there were still uncertainties in the role of government response. How to entirely separate the contribution of social-distancing from endogenous immune drivers is still a challenge. Secondly, our data were obtained from daily reports, in which the individual clinical information (e.g., channels of infection, age, and burden of chronic diseases) was missing. Therefore, the heterogeneity in individuals was not considered. Thirdly, our conclusions are drawn on statistical models, but it still requires epidemiological analysis or random control experiment to explore the effect of weather. Finally, we would explorer the underlying non-linear effect of temperature on COVID-19 transmission in future with more available datasets. 4. Method 4.1 Statistical Analysis We built a multivariate regression model (see Eq. (1)) to explore the weather condition effect on transmission: , = (,−Δ ) + ,−Δ + ,−Δ + + + + + + ℎ + + (1) , = (,, − ,−1 )/ 2 (2) 0,, = 1 + , + ℎ(1 − ℎ)(, ) (3) , = (, )/ (4) where i indexes a region, t a day, and Δ a lag day. We considered the new daily cases fraction () and the basic reproductive number (R0) as dependent variables (Y) in Eq. (1). R0 is calculated as in Eq. (3) and (4), where the mean serial interval V, exponential growth rate λ of the cumulative number of cases (confirmed), and ratio of the infectious period to the serial interval h are set following 21. Accordingly, daily average temperature (Tmean, in Celsius degree) and relative humidity (%, RH) are our variables of interest. Here, (,−Δ ) refers to flexible functional forms of temperature including higher degree polynomial and splines, thereby allowing for a possible non-linear relationship. RH is a control for evaporation and affects a droplet’s size and its chemical microenvironment22. Therefore, it is RH (but not absolute humidity) that acts as a determinant factor for virus survival in aerosols. To the best of the authors’ knowledge, individuals who get infected are likely to experience an incubation period before onset. Current evidences23-24 suggest that the incubation period may vary between 6 and 8 days. Accordingly, we focused on the effects of temperature and relative humidity with a 6-day lag and further examined the same effects with 5-day to 14-day lags for control groups. There are a number of obvious confounding factors (e.g., active case fraction, economic development, population concentration25, age structure26, geographic conditions27, and government intervention18) that affect the transmission of an epidemic, so they should be controlled in the regression analysis. On this basis, the control variables should include gross regional product per capital (GRPper), regional population concentration (PopCon), government response (Lockdown), elevation, and suspected population. The last one is composed of the working population (aged 15-64) ratio () and school age group (aged 6-15) ratio (ℎ).The role of GRP is of uncertainty in COVID-19 transmission. A higher ita means closer social distance and more frequent population movement while it also denotes higher education attainment and better cognition on COVID-19. Hospital condition is conventionally to measure medical quality on disease cure but little on prevention. Hospital condition may have significant impacts on death cases but little on new cases. Therefore, we do not add the hospital condition in the control variables. Some of listed control variables (work age and school age population) are related to social distancing. Additionally, we added time fixed effects as a control for factors that are common to all countries, such as the global virus prevention materials supply (e.g. ethanol, mask, and protective suit) and public awareness of COVID-19 at different stages. It is widely accepted that government response is a vital factor for COVID-19 transmission 18, 28-29, of which local and trans-regional transmission are two possible outbreak channels. Note that the measures taken by governments across the globe have affected public movement greatly, such as border controls, teleworking from home, social distancing and limiting the sizes of gatherings. Correspondingly, we added a variable Lockdown, which was a control of government intervention in local and trans-regional COVID-19 transmission through social distancing and trans- border flow. However, due to data limitations, it is difficult to accurately evaluate the contribution of government response to COVID-19 transmission. Here, we assumed that its contribution would increase as the policy continues to take effect. Nevertheless, it is not likely to increase infinitely, i.e., the contribution rate will slow down when approaching its peak. Under this hypothesis, we apply a logistic transforming function to evaluate government response. An additional explanation for lockdown can be found in supplementary information. To further explore the effect of weather conditions on transmission by income group, we divided the samples by GRP per groups in accordance with the World Bank criterion, and construct a dummy variable high with high=1 indicating high-income regions and high=0 low-income regions. This dummy variable and its interactions with Tmean and RH are both added to the models. Afterwards, we merged the weather variables into the SIER model. Details about the model specification can be found in the supplementary information, where Stata 14/MP was used to perform the multi-variate regression analysis. 4.2 Robustness Checks To reduce the possibility of selective bias on some key variables, we conducted three robustness checks for the weather-transmission relationship: (1) The selection of threshold of total regional confirmed cases for observations is of vital to the estimation. Here, we examined the relationships by increasing the threshold of total case numbers to 200 and 300, respectively. (2) Considering that daily temperature differences between the maximum and minimum temperature vary across regions globally, we substituted average temperature by its maximum and minimum counterparts, separately. (3) Multi-initial values of lockdown in logistic functions were applied to prove that its initial value can affect weather-transmission relationship. 4.3 Source of Data We collected the new daily cases, cured, and deaths in 1,236 regions in the world as of 31 May, 2020, which were extracted from the COVID-19 epidemic information released by public available daily COVID-19 reports from the official health department of countries. To deal with small countries that lack sub-national case data, whose average land areas are about 185,000 km2 and among which the largest one is Algeria (2,382,000 km2), we selected alternative country-level data from the COVID-19 Data Repository established by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. Finally, our sample covered 5,926,622 confirmed cases and 7.4 billon of the global population, which are equal to 98.7% of global confirmed cases and 98.2% of the global population, respectively. Our study area is comprised of 1,112 sub-national regions (in 57 countries) and 124 countries (Figure 2). The sources of remote sensing satellite data, as well as weather and social-economic features, are listed in Supplement table S1.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: Please consider improving the rainbow (“jet”) colormap used on page 16. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
-
SciScore for 10.1101/2020.07.29.20164152: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources Met hod4.1Stat ist icalAnalysisW eb uiltamultiv ari ate regressionm odel(s eeE q.(1))toexp lorethew ea therc o nditioneffec tontransmi ssion :,=( ,−Δ ) +,− Δ+ ,−Δ++++ ++ℎ ++(1),= (,,−,−1)/ 2(2)0, ,= 1+,+ℎ(1−ℎ)(,) (3), = (,)/(4)wherei i n dexesare g ion,taday,andΔ a lagday. ,−Δ ) + ,−Δ + ,−Δ + + + + + + ℎ + +suggested: NoneResults from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data …
SciScore for 10.1101/2020.07.29.20164152: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources Met hod4.1Stat ist icalAnalysisW eb uiltamultiv ari ate regressionm odel(s eeE q.(1))toexp lorethew ea therc o nditioneffec tontransmi ssion :,=( ,−Δ ) +,− Δ+ ,−Δ++++ ++ℎ ++(1),= (,,−,−1)/ 2(2)0, ,= 1+,+ℎ(1−ℎ)(,) (3), = (,)/(4)wherei i n dexesare g ion,taday,andΔ a lagday. ,−Δ ) + ,−Δ + ,−Δ + + + + + + ℎ + +suggested: NoneResults from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
Some limitations of the present study should also be pointed out. First, our conclusions were drawn based on observations over certain periods, thus there were still uncertainties in the role of government response. How to entirely separate the contribution of social-distancing from endogenous immune drivers is still a challenge. Secondly, our data were obtained from daily reports, in which the individual clinical information (e.g., channels of infection, age, and burden of chronic diseases) was missing. Therefore, the heterogeneity in individuals was not considered. Thirdly, our conclusions are drawn on statistical models, but it still requires epidemiological analysis or random control experiment to explore the effect of weather. Finally, we would explorer the underlying non-linear effect of temperature on COVID-19 transmission in future with more available datasets. 4. Method 4.1 Statistical Analysis We built a multivariate regression model (see Eq. (1)) to explore the weather condition effect on transmission: , = (,−Δ ) + ,−Δ + ,−Δ + + + + + + ℎ + + (1) , = (,, − ,−1 )/ 2 (2) 0,, = 1 + , + ℎ(1 − ℎ)(, ) (3) , = (, )/ (4) where i indexes a region, t a day, and Δ a lag day. We considered the new daily cases fraction () and the basic reproductive number (R0) as dependent variables (Y) in Eq. (1). R0 is calculated as in Eq. (3) and (4), where the mean serial interval V, exponential growth rate λ of the cumulative number of cases (confirmed), and ratio of the infectious period to the serial interval h are set following 21. Accordingly, daily average temperature (Tmean, in Celsius degree) and relative humidity (%, RH) are our variables of interest. Here, (,−Δ ) refers to flexible functional forms of temperature including higher degree polynomial and splines, thereby allowing for a possible non-linear relationship. RH is a control for evaporation and affects a droplet’s size and its chemical microenvironment22. Therefore, it is RH (but not absolute humidity) that acts as a determinant factor for virus survival in aerosols. To the best of the authors’ knowledge, individuals who get infected are likely to experience an incubation period before onset. Current evidences23-24 suggest that the incubation period may vary between 6 and 8 days. Accordingly, we focused on the effects of temperature and relative humidity with a 6-day lag and further examined the same effects with 5-day to 14-day lags for control groups. There are a number of obvious confounding factors (e.g., active case fraction, economic development, population concentration25, age structure26, geographic conditions27, and government intervention18) that affect the transmission of an epidemic, so they should be controlled in the regression analysis. On this basis, the control variables should include gross regional product per capital (GRPper), regional population concentration (PopCon), government response (Lockdown), elevation, and suspected population. The last one is composed of the working population (aged 15-64) ratio () and school age group (aged 6-15) ratio (ℎ).The role of GRP is of uncertainty in COVID-19 transmission. A higher ita means closer social distance and more frequent population movement while it also denotes higher education attainment and better cognition on COVID-19. Hospital condition is conventionally to measure medical quality on disease cure but little on prevention. Hospital condition may have significant impacts on death cases but little on new cases. Therefore, we do not add the hospital condition in the control variables. Some of listed control variables (work age and school age population) are related to social distancing. Additionally, we added time fixed effects as a control for factors that are common to all countries, such as the global virus prevention materials supply (e.g. ethanol, mask, and protective suit) and public awareness of COVID-19 at different stages. It is widely accepted that government response is a vital factor for COVID-19 transmission 18, 28-29, of which local and trans-regional transmission are two possible outbreak channels. Note that the measures taken by governments across the globe have affected public movement greatly, such as border controls, teleworking from home, social distancing and limiting the sizes of gatherings. Correspondingly, we added a variable Lockdown, which was a control of government intervention in local and trans-regional COVID-19 transmission through social distancing and trans- border flow. However, due to data limitations, it is difficult to accurately evaluate the contribution of government response to COVID-19 transmission. Here, we assumed that its contribution would increase as the policy continues to take effect. Nevertheless, it is not likely to increase infinitely, i.e., the contribution rate will slow down when approaching its peak. Under this hypothesis, we apply a logistic transforming function to evaluate government response. An additional explanation for lockdown can be found in supplementary information. To further explore the effect of weather conditions on transmission by income group, we divided the samples by GRP per groups in accordance with the World Bank criterion, and construct a dummy variable high with high=1 indicating high-income regions and high=0 low-income regions. This dummy variable and its interactions with Tmean and RH are both added to the models. Afterwards, we merged the weather variables into the SIER model. Details about the model specification can be found in the supplementary information, where Stata 14/MP was used to perform the multi-variate regression analysis. 4.2 Robustness Checks To reduce the possibility of selective bias on some key variables, we conducted three robustness checks for the weather-transmission relationship: (1) The selection of threshold of total regional confirmed cases for observations is of vital to the estimation. Here, we examined the relationships by increasing the threshold of total case numbers to 200 and 300, respectively. (2) Considering that daily temperature differences between the maximum and minimum temperature vary across regions globally, we substituted average temperature by its maximum and minimum counterparts, separately. (3) Multi-initial values of lockdown in logistic functions were applied to prove that its initial value can affect weather-transmission relationship. 4.3 Source of Data We collected the new daily cases, cured, and deaths in 1,236 regions in the world as of 31 May, 2020, which were extracted from the COVID-19 epidemic information released by public available daily COVID-19 reports from the official health department of countries. To deal with small countries that lack sub-national case data, whose average land areas are about 185,000 km2 and among which the largest one is Algeria (2,382,000 km2), we selected alternative country-level data from the COVID-19 Data Repository established by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. Finally, our sample covered 5,926,622 confirmed cases and 7.4 billon of the global population, which are equal to 98.7% of global confirmed cases and 98.2% of the global population, respectively. Our study area is comprised of 1,112 sub-national regions (in 57 countries) and 124 countries (Figure 2). The sources of remote sensing satellite data, as well as weather and social-economic features, are listed in Supplement table S1.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: Please consider improving the rainbow (“jet”) colormap used on page 16. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
-
SciScore for 10.1101/2020.07.29.20164152: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources Met hod4.1Stat ist icalAnalysisW eb uiltamultiv ari ate regressionm odel(s eeE q.(1))toexp lorethew ea therc o nditioneffec tontransmi ssion :,=( ,−Δ ) +,− Δ+ ,−Δ++++ ++ℎ ++(1),= (,,−,−1)/ 2(2)0, ,= 1+,+ℎ(1−ℎ)(,) (3), = (,)/(4)wherei i n dexesare g ion,taday,andΔ a lagday. ,−Δ ) + ,−Δ + ,−Δ + + + + + + ℎ + +suggested: NoneResults from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data …
SciScore for 10.1101/2020.07.29.20164152: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources Met hod4.1Stat ist icalAnalysisW eb uiltamultiv ari ate regressionm odel(s eeE q.(1))toexp lorethew ea therc o nditioneffec tontransmi ssion :,=( ,−Δ ) +,− Δ+ ,−Δ++++ ++ℎ ++(1),= (,,−,−1)/ 2(2)0, ,= 1+,+ℎ(1−ℎ)(,) (3), = (,)/(4)wherei i n dexesare g ion,taday,andΔ a lagday. ,−Δ ) + ,−Δ + ,−Δ + + + + + + ℎ + +suggested: NoneResults from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
Some limitations of the present study should also be pointed out. First, our conclusions were drawn based on observations over certain periods, thus there were still uncertainties in the role of government response. How to entirely separate the contribution of social-distancing from endogenous immune drivers is still a challenge. Secondly, our data were obtained from daily reports, in which the individual clinical information (e.g., channels of infection, age, and burden of chronic diseases) was missing. Therefore, the heterogeneity in individuals was not considered. Thirdly, our conclusions are drawn on statistical models, but it still requires epidemiological analysis or random control experiment to explore the effect of weather. Finally, we would explorer the underlying non-linear effect of temperature on COVID-19 transmission in future with more available datasets. 4. Method 4.1 Statistical Analysis We built a multivariate regression model (see Eq. (1)) to explore the weather condition effect on transmission: , = (,−Δ ) + ,−Δ + ,−Δ + + + + + + ℎ + + (1) , = (,, − ,−1 )/ 2 (2) 0,, = 1 + , + ℎ(1 − ℎ)(, ) (3) , = (, )/ (4) where i indexes a region, t a day, and Δ a lag day. We considered the new daily cases fraction () and the basic reproductive number (R0) as dependent variables (Y) in Eq. (1). R0 is calculated as in Eq. (3) and (4), where the mean serial interval V, exponential growth rate λ of the cumulative number of cases (confirmed), and ratio of the infectious period to the serial interval h are set following 21. Accordingly, daily average temperature (Tmean, in Celsius degree) and relative humidity (%, RH) are our variables of interest. Here, (,−Δ ) refers to flexible functional forms of temperature including higher degree polynomial and splines, thereby allowing for a possible non-linear relationship. RH is a control for evaporation and affects a droplet’s size and its chemical microenvironment22. Therefore, it is RH (but not absolute humidity) that acts as a determinant factor for virus survival in aerosols. To the best of the authors’ knowledge, individuals who get infected are likely to experience an incubation period before onset. Current evidences23-24 suggest that the incubation period may vary between 6 and 8 days. Accordingly, we focused on the effects of temperature and relative humidity with a 6-day lag and further examined the same effects with 5-day to 14-day lags for control groups. There are a number of obvious confounding factors (e.g., active case fraction, economic development, population concentration25, age structure26, geographic conditions27, and government intervention18) that affect the transmission of an epidemic, so they should be controlled in the regression analysis. On this basis, the control variables should include gross regional product per capital (GRPper), regional population concentration (PopCon), government response (Lockdown), elevation, and suspected population. The last one is composed of the working population (aged 15-64) ratio () and school age group (aged 6-15) ratio (ℎ).The role of GRP is of uncertainty in COVID-19 transmission. A higher ita means closer social distance and more frequent population movement while it also denotes higher education attainment and better cognition on COVID-19. Hospital condition is conventionally to measure medical quality on disease cure but little on prevention. Hospital condition may have significant impacts on death cases but little on new cases. Therefore, we do not add the hospital condition in the control variables. Some of listed control variables (work age and school age population) are related to social distancing. Additionally, we added time fixed effects as a control for factors that are common to all countries, such as the global virus prevention materials supply (e.g. ethanol, mask, and protective suit) and public awareness of COVID-19 at different stages. It is widely accepted that government response is a vital factor for COVID-19 transmission 18, 28-29, of which local and trans-regional transmission are two possible outbreak channels. Note that the measures taken by governments across the globe have affected public movement greatly, such as border controls, teleworking from home, social distancing and limiting the sizes of gatherings. Correspondingly, we added a variable Lockdown, which was a control of government intervention in local and trans-regional COVID-19 transmission through social distancing and trans- border flow. However, due to data limitations, it is difficult to accurately evaluate the contribution of government response to COVID-19 transmission. Here, we assumed that its contribution would increase as the policy continues to take effect. Nevertheless, it is not likely to increase infinitely, i.e., the contribution rate will slow down when approaching its peak. Under this hypothesis, we apply a logistic transforming function to evaluate government response. An additional explanation for lockdown can be found in supplementary information. To further explore the effect of weather conditions on transmission by income group, we divided the samples by GRP per groups in accordance with the World Bank criterion, and construct a dummy variable high with high=1 indicating high-income regions and high=0 low-income regions. This dummy variable and its interactions with Tmean and RH are both added to the models. Afterwards, we merged the weather variables into the SIER model. Details about the model specification can be found in the supplementary information, where Stata 14/MP was used to perform the multi-variate regression analysis. 4.2 Robustness Checks To reduce the possibility of selective bias on some key variables, we conducted three robustness checks for the weather-transmission relationship: (1) The selection of threshold of total regional confirmed cases for observations is of vital to the estimation. Here, we examined the relationships by increasing the threshold of total case numbers to 200 and 300, respectively. (2) Considering that daily temperature differences between the maximum and minimum temperature vary across regions globally, we substituted average temperature by its maximum and minimum counterparts, separately. (3) Multi-initial values of lockdown in logistic functions were applied to prove that its initial value can affect weather-transmission relationship. 4.3 Source of Data We collected the new daily cases, cured, and deaths in 1,236 regions in the world as of 31 May, 2020, which were extracted from the COVID-19 epidemic information released by public available daily COVID-19 reports from the official health department of countries. To deal with small countries that lack sub-national case data, whose average land areas are about 185,000 km2 and among which the largest one is Algeria (2,382,000 km2), we selected alternative country-level data from the COVID-19 Data Repository established by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. Finally, our sample covered 5,926,622 confirmed cases and 7.4 billon of the global population, which are equal to 98.7% of global confirmed cases and 98.2% of the global population, respectively. Our study area is comprised of 1,112 sub-national regions (in 57 countries) and 124 countries (Figure 2). The sources of remote sensing satellite data, as well as weather and social-economic features, are listed in Supplement table S1.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: Please consider improving the rainbow (“jet”) colormap used on page 16. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
-
SciScore for 10.1101/2020.07.29.20164152: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources Met hod4.1Stat ist icalAnalysisW eb uiltamultiv ari ate regressionm odel(s eeE q.(1))toexp lorethew ea therc o nditioneffec tontransmi ssion :,=( ,−Δ ) +,− Δ+ ,−Δ++++ ++ℎ ++(1),= (,,−,−1)/ 2(2)0, ,= 1+,+ℎ(1−ℎ)(,) (3), = (,)/(4)wherei i n dexesare g ion,taday,andΔ a lagday. ,−Δ ) + ,−Δ + ,−Δ + + + + + + ℎ + +suggested: NoneResults from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data …
SciScore for 10.1101/2020.07.29.20164152: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources Met hod4.1Stat ist icalAnalysisW eb uiltamultiv ari ate regressionm odel(s eeE q.(1))toexp lorethew ea therc o nditioneffec tontransmi ssion :,=( ,−Δ ) +,− Δ+ ,−Δ++++ ++ℎ ++(1),= (,,−,−1)/ 2(2)0, ,= 1+,+ℎ(1−ℎ)(,) (3), = (,)/(4)wherei i n dexesare g ion,taday,andΔ a lagday. ,−Δ ) + ,−Δ + ,−Δ + + + + + + ℎ + +suggested: NoneResults from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
Some limitations of the present study should also be pointed out. First, our conclusions were drawn based on observations over certain periods, thus there were still uncertainties in the role of government response. How to entirely separate the contribution of social-distancing from endogenous immune drivers is still a challenge. Secondly, our data were obtained from daily reports, in which the individual clinical information (e.g., channels of infection, age, and burden of chronic diseases) was missing. Therefore, the heterogeneity in individuals was not considered. Thirdly, our conclusions are drawn on statistical models, but it still requires epidemiological analysis or random control experiment to explore the effect of weather. Finally, we would explorer the underlying non-linear effect of temperature on COVID-19 transmission in future with more available datasets. 4. Method 4.1 Statistical Analysis We built a multivariate regression model (see Eq. (1)) to explore the weather condition effect on transmission: , = (,−Δ ) + ,−Δ + ,−Δ + + + + + + ℎ + + (1) , = (,, − ,−1 )/ 2 (2) 0,, = 1 + , + ℎ(1 − ℎ)(, ) (3) , = (, )/ (4) where i indexes a region, t a day, and Δ a lag day. We considered the new daily cases fraction () and the basic reproductive number (R0) as dependent variables (Y) in Eq. (1). R0 is calculated as in Eq. (3) and (4), where the mean serial interval V, exponential growth rate λ of the cumulative number of cases (confirmed), and ratio of the infectious period to the serial interval h are set following 21. Accordingly, daily average temperature (Tmean, in Celsius degree) and relative humidity (%, RH) are our variables of interest. Here, (,−Δ ) refers to flexible functional forms of temperature including higher degree polynomial and splines, thereby allowing for a possible non-linear relationship. RH is a control for evaporation and affects a droplet’s size and its chemical microenvironment22. Therefore, it is RH (but not absolute humidity) that acts as a determinant factor for virus survival in aerosols. To the best of the authors’ knowledge, individuals who get infected are likely to experience an incubation period before onset. Current evidences23-24 suggest that the incubation period may vary between 6 and 8 days. Accordingly, we focused on the effects of temperature and relative humidity with a 6-day lag and further examined the same effects with 5-day to 14-day lags for control groups. There are a number of obvious confounding factors (e.g., active case fraction, economic development, population concentration25, age structure26, geographic conditions27, and government intervention18) that affect the transmission of an epidemic, so they should be controlled in the regression analysis. On this basis, the control variables should include gross regional product per capital (GRPper), regional population concentration (PopCon), government response (Lockdown), elevation, and suspected population. The last one is composed of the working population (aged 15-64) ratio () and school age group (aged 6-15) ratio (ℎ).The role of GRP is of uncertainty in COVID-19 transmission. A higher ita means closer social distance and more frequent population movement while it also denotes higher education attainment and better cognition on COVID-19. Hospital condition is conventionally to measure medical quality on disease cure but little on prevention. Hospital condition may have significant impacts on death cases but little on new cases. Therefore, we do not add the hospital condition in the control variables. Some of listed control variables (work age and school age population) are related to social distancing. Additionally, we added time fixed effects as a control for factors that are common to all countries, such as the global virus prevention materials supply (e.g. ethanol, mask, and protective suit) and public awareness of COVID-19 at different stages. It is widely accepted that government response is a vital factor for COVID-19 transmission 18, 28-29, of which local and trans-regional transmission are two possible outbreak channels. Note that the measures taken by governments across the globe have affected public movement greatly, such as border controls, teleworking from home, social distancing and limiting the sizes of gatherings. Correspondingly, we added a variable Lockdown, which was a control of government intervention in local and trans-regional COVID-19 transmission through social distancing and trans- border flow. However, due to data limitations, it is difficult to accurately evaluate the contribution of government response to COVID-19 transmission. Here, we assumed that its contribution would increase as the policy continues to take effect. Nevertheless, it is not likely to increase infinitely, i.e., the contribution rate will slow down when approaching its peak. Under this hypothesis, we apply a logistic transforming function to evaluate government response. An additional explanation for lockdown can be found in supplementary information. To further explore the effect of weather conditions on transmission by income group, we divided the samples by GRP per groups in accordance with the World Bank criterion, and construct a dummy variable high with high=1 indicating high-income regions and high=0 low-income regions. This dummy variable and its interactions with Tmean and RH are both added to the models. Afterwards, we merged the weather variables into the SIER model. Details about the model specification can be found in the supplementary information, where Stata 14/MP was used to perform the multi-variate regression analysis. 4.2 Robustness Checks To reduce the possibility of selective bias on some key variables, we conducted three robustness checks for the weather-transmission relationship: (1) The selection of threshold of total regional confirmed cases for observations is of vital to the estimation. Here, we examined the relationships by increasing the threshold of total case numbers to 200 and 300, respectively. (2) Considering that daily temperature differences between the maximum and minimum temperature vary across regions globally, we substituted average temperature by its maximum and minimum counterparts, separately. (3) Multi-initial values of lockdown in logistic functions were applied to prove that its initial value can affect weather-transmission relationship. 4.3 Source of Data We collected the new daily cases, cured, and deaths in 1,236 regions in the world as of 31 May, 2020, which were extracted from the COVID-19 epidemic information released by public available daily COVID-19 reports from the official health department of countries. To deal with small countries that lack sub-national case data, whose average land areas are about 185,000 km2 and among which the largest one is Algeria (2,382,000 km2), we selected alternative country-level data from the COVID-19 Data Repository established by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. Finally, our sample covered 5,926,622 confirmed cases and 7.4 billon of the global population, which are equal to 98.7% of global confirmed cases and 98.2% of the global population, respectively. Our study area is comprised of 1,112 sub-national regions (in 57 countries) and 124 countries (Figure 2). The sources of remote sensing satellite data, as well as weather and social-economic features, are listed in Supplement table S1.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: Please consider improving the rainbow (“jet”) colormap used on page 16. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
-