Tracking [Formula: see text] of COVID-19: A new real-time estimation using the Kalman filter

Mutual relationships between SARS-CoV-2 test numbers, fatality and morbidity rates

BACKGROUND

The number of SARS-CoV-2 tests conversely to other factors, such as age of population or comorbidities, influencing SARS-CoV-2 morbidity and fatality rates, can be increased or decreased by decision makers depending on the development of the pandemic, operational capacity, and financial restraints. The key objective of this study is to identify and describe, within the probabilistic approach, the relationships between SARS-CoV-2 test numbers and the mortality and morbidity rates.

METHODS

The study is based on a statistical analysis of 1058 monthly observations relating to 107 countries, from six different continents, in an 11-month period from March 2020 to January 2021. The variable utilised can be defined as the number of tests performed in a given country in 1 month, to the number of cases reported in a prior month and morbidities and mortalities per 1 million population. The probabilities of different mortality and morbidity rates for different test numbers were determined by moving percentiles and fitted by the power law and by the three-segment piecewise-linear approximation based on Theil Sen trend lines.

RESULTS

We have identified that for a given probability the dependence of mortality and morbidity rates on SARS-CoV-2 test rates follows a power law and it is well approximated by the three Theil Sen trend lines in the three test rate ranges. In all these ranges Spearman rho and Kendall tau-b rank correlation coefficients of test numbers and morbidity with fatality rates have values between - 0.5 and - 0.12 with p-values below 0.002.

CONCLUSIONS

According to the ABC classification: the most important, moderately important, and relatively unimportant ranges of test numbers for managing and control have been indicated based on the value of the Theil Sen trend line slope in the three SARS-CoV-2 test rate ranges identified. Recommendations for SARS-CoV-2 testing strategy are provided.

Mediation by human mobility of the association between temperature and COVID-19 transmission rate

The coronavirus disease 2019 (COVID-19) pandemic is a major threat to global health. Relevant studies have shown that ambient temperature may influence the spread of novel coronavirus. However, the effect of ambient temperature on COVID-19 remains controversial. Human mobility is also closely related to the pandemic of COVID-19, which could be affected by temperature at the same time. The purpose of this study is to explore the underlying mechanism of the association of temperature with COVID-19 transmission rate by linking human mobility. The effective reproductive number, meteorological conditions and human mobility data in 47 countries are collected. Panel data models with fixed effects are used to analyze the association of ambient temperature with COVID-19 transmission rate, and the mediation by human mobility. Our results show that there is a negative relationship between temperature and COVID-19 transmission rate. We also observe that temperature is positively associated with human mobility and human mobility is positively related to COVID-19 transmission rate. Thus, the suppression effect (also known as the inconsistent mediation effect) of human mobility is confirmed, which remains robust when different lag structures are used. These findings provide evidence that temperature can influence the spread of COVID-19 by affecting human mobility. Therefore, although temperature is negatively related to COVID-19 transmission rate, governments and the public should pay more attention to control measures since people are more likely to go out when temperature rising. Our results could partially explain the reason why COVID-19 is not prevented by warm weather in some countries.