Assessing the effectiveness of non-pharmaceutical interventions (NPIs) to mitigate the spread of SARS-CoV-2 is critical to inform future preparedness response plans. Here we quantify the impact of 6,068 hierarchically coded NPIs implemented in 79 territories on the effective reproduction number, Rt, of COVID-19. We propose a modelling approach that combines four computational techniques merging statistical, inference and artificial intelligence tools. We validate our findings with two external datasets recording 42,151 additional NPIs from 226 countries. Our results indicate that a suitable combination of NPIs is necessary to curb the spread of the virus. Less disruptive and costly NPIs can be as effective as more intrusive, drastic, ones (for example, a national lockdown). Using country-specific ‘what-if’ scenarios, we assess how the effectiveness of NPIs depends on the local context such as timing of their adoption, opening the way for forecasting the effectiveness of future interventions. Analysing over 50,000 government interventions in more than 200 countries, Haug et al. find that combinations of softer measures, such as risk communication or those increasing healthcare capacity, can be almost as effective as disruptive lockdowns.

/pdf/ranking-the-effectiveness-of-worldwide-covid-19-government-2q47z7xuir.pdf

Ranking the effectiveness of worldwide COVID-19 government interventions.

Transmission of COVID-19 virus by droplets and aerosols: A critical review on the unresolved dichotomy.

Correlation between weather and Covid-19 pandemic in Jakarta, Indonesia.

GIS-based spatial modeling of COVID-19 incidence rate in the continental United States.

Investigation of effective climatology parameters on COVID-19 outbreak in Iran.

With the ongoing global pandemic of COVID-19, a question is whether the coming summer in the northern hemisphere will reduce the transmission intensity of COVID-19 with increased humidity and temperature. In this paper, we investigate this problem using the data from the cases with symptom-onset dates from January 19 to February 10, 2020 for 100 Chinese cities, and cases with confirmed dates from March 15 to April 25 for 1,005 U.S. counties. Statistical analysis is performed to assess the relationship between the transmissibility of COVID-19 and the temperature/humidity, by controlling for various demographic, socio-economic, geographic, healthcare and policy factors and correcting for cross-sectional correlation. We find a similar influence of the temperature and relative humidity on effective reproductive number (R values) of COVID-19 for both China and the U.S. before lockdown in both countries: one-degree Celsius increase in temperature reduces R value by about 0.023 (0.026 (95% CI [-0.0395,-0.0125]) in China and 0.020 (95% CI [-0.0311, -0.0096]) in the U.S.), and one percent relative humidity rise reduces R value by 0.0078 (0.0076 (95% CI [-0.0108,-0.0045]) in China and 0.0080 (95% CI [-0.0150,-0.0010]) in the U.S.). If assuming a 30 degree and 25 percent increase in temperature and relative humidity from winter to summer in the northern hemisphere, we expect the R values to decline about 0.89 (0.69 by temperature and 0.20 by humidity). Moreover, after the lockdowns in China and the U.S., temperature and relative humidity still play an important role in reducing the R values but to a less extent. Given the notion that the non-intervened R values are around 2.5 to 3, only weather factors cannot make the R values below their critical condition of R<1, under which the epidemic diminishes gradually. Therefore, public health intervention such as social distancing is crucial to block the transmission of COVID-19 even in summer.

/pdf/high-temperature-and-high-humidity-reduce-the-transmission-2wbd3oxowl.pdf

High Temperature and High Humidity Reduce the Transmission of COVID-19

Objectives We aim to assess the impact of temperature and relative humidity on the transmission of COVID-19 across communities after accounting for community-level factors such as demographics, socioeconomic status and human mobility status. Design A retrospective cross-sectional regression analysis via the Fama-MacBeth procedure is adopted. Setting We use the data for COVID-19 daily symptom-onset cases for 100 Chinese cities and COVID-19 daily confirmed cases for 1005 US counties. Participants A total of 69 498 cases in China and 740 843 cases in the USA are used for calculating the effective reproductive numbers. Primary outcome measures Regression analysis of the impact of temperature and relative humidity on the effective reproductive number (R value). Results Statistically significant negative correlations are found between temperature/relative humidity and the effective reproductive number (R value) in both China and the USA. Conclusions Higher temperature and higher relative humidity potentially suppress the transmission of COVID-19. Specifically, an increase in temperature by 1°C is associated with a reduction in the R value of COVID-19 by 0.026 (95% CI (−0.0395 to −0.0125)) in China and by 0.020 (95% CI (−0.0311 to −0.0096)) in the USA; an increase in relative humidity by 1% is associated with a reduction in the R value by 0.0076 (95% CI (−0.0108 to −0.0045)) in China and by 0.0080 (95% CI (−0.0150 to −0.0010)) in the USA. Therefore, the potential impact of temperature/relative humidity on the effective reproductive number alone is not strong enough to stop the pandemic.

https://bmjopen.bmj.com/content/bmjopen/11/2/e043863.full.pdf

Impact of temperature and relative humidity on the transmission of COVID-19: a modelling study in China and the United States.

With the ongoing global pandemic of COVID-19, a question is whether the coming summer in the northern hemisphere will reduce the transmission intensity of COVID-19 with increased humidity and temperature. In this paper, we investigate this problem using the data from the cases with symptom-onset dates from January 19 to February 10, 2020 for 100 Chinese cities, and cases with confirmed dates from March 15 to April 25 for 1,005 U.S. counties. Statistical analysis is performed to assess the relationship between the transmissibility of COVID-19 and the temperature/humidity, by controlling for various demographic, socio-economic, geographic, healthcare and policy factors and correcting for cross-sectional correlation. We find a similar influence of the temperature and relative humidity on effective reproductive number (R values) of COVID-19 for both China and the U.S. before lockdown in both countries: one-degree Celsius increase in temperature reduces R value by about 0.023 (0.026 (95% CI [-0.0395,-0.0125]) in China and 0.020 (95% CI [-0.0311, -0.0096]) in the U.S.), and one percent relative humidity rise reduces R value by 0.0078 (0.0076 (95% CI [-0.0108,-0.0045]) in China and 0.0080 (95% CI [-0.0150,-0.0010]) in the U.S.). If assuming a 30 degree and 25 percent increase in temperature and relative humidity from winter to summer in the northern hemisphere, we expect the R values to decline about 0.89 (0.69 by temperature and 0.20 by humidity). Given the notion that the non-intervened R values are around 2.5 to 3, only weather factors cannot make the R values below their critical condition of R<1, under which the epidemic diminishes gradually. Therefore, public health intervention such as social distancing is crucial to block the transmission of COVID-19 even in summer.

As more and more countries have employed stay-at-home policy to halt the spread of COVID-19, the effectiveness of this policy has become an important question to both researchers and policymakers. To answer this question, our paper empirically measures the effect of stay-at-home policy on the control of COVID-19. Using the city-level Baidu Mobility Index, measured by the total number of outside travels per day divided by the resident population, we find that reducing the number of outings can effectively decrease the new-onset cases; a 1% decline in the outing number will reduce about 1% of the new-onset-cases growth rate in 7 days (one serial interval). The critical level is a 50% drop in mobility, in which case the number of new-onset cases is lower than it was 7 days before, and hence the epidemic will gradually disappear holding this policy long enough. A strong stay-at-home policy execution with a short duration has a smaller economic cost than a loose execution with a long duration. For example, the mobility in Wuhan is down 85% after lockdown, in which case we estimate the number of new-onset cases is reduced by 50% in only 12 days.

When is the COVID-19 Pandemic Over? Evidence from the Stay-at-Home Policy Execution in 106 Chinese Cities

The Receiver Operating Characteristic (ROC) curve is a representation of the statistical information discovered in binary classification problems and is a key concept in machine learning and data science. This paper studies the statistical properties of ROC curves and its implication on model selection. We analyze the implications of different models of incentive heterogeneity and information asymmetry on the relation between human decisions and the ROC curves. Our theoretical discussion is illustrated in the context of a large data set of pregnancy outcomes and doctor diagnosis from the Pre-Pregnancy Checkups of reproductive age couples in Henan Province provided by the Chinese Ministry of Health.

Kai Feng

Papers

High Temperature and High Humidity Reduce the Transmission of COVID-19

Impact of temperature and relative humidity on the transmission of COVID-19: a modelling study in China and the United States.

High Temperature and High Humidity Reduce the Transmission of COVID-19

When is the COVID-19 Pandemic Over? Evidence from the Stay-at-Home Policy Execution in 106 Chinese Cities

Decision Making with Machine Learning and ROC Curves.