In-Depth Correlation Analysis of SARS-CoV-2 Effective Reproduction Number and Mobility Patterns: Three Groups of Countries

The effect of mobility reductions on infection growth is quadratic in many cases

Stay-at-home orders were introduced in many countries during the COVID-19 pandemic, limiting the time people spent outside their home and the attendance of gatherings. In this study, we argue from a theoretical model that in many cases the effect of such stay-at-home orders on incidence growth should be quadratic, and that this statement should also hold beyond COVID-19. That is, a reduction of the out-of-home duration to, say, 70% of its original value should reduce incidence growth and thus the effective R-value to 70 % · 70 % = 49 % of its original value. We then show that this hypothesis can be substantiated from data acquired during the COVID-19 pandemic by using a multiple regression model to fit a combination of the quadratic out-of-home duration and temperature to the COVID-19 growth multiplier. We finally demonstrate that many other models, when brought to the same scale, give similar reductions of the effective R-value, but that none of these models extend plausibly to an out-of-home duration of zero.

The effects of weather and mobility on respiratory viruses dynamics before and during the COVID-19 pandemic in the USA and Canada

The flu season is caused by a combination of different pathogens, including influenza viruses (IVS), that cause the flu, and non-influenza respiratory viruses (NIRVs), that cause common colds or influenza-like illness. These viruses exhibit similar dynamics and meteorological conditions have historically been regarded as a principal modulator of their epidemiology, with outbreaks in the winter and almost no circulation during the summer, in temperate regions. However, after the emergence of SARS-CoV2, in late 2019, the dynamics of these respiratory viruses were strongly perturbed worldwide: some infections displayed near-eradication, while others experienced temporal shifts or occurred "off-season". This disruption raised questions regarding the dominant role of weather while also providing an unique opportunity to investigate the roles of different determinants on the epidemiological dynamics of IVs and NIRVs. Here, we employ statistical analysis and modelling to test the effects of weather and mobility in viral dynamics, before and during the COVID-19 pandemic. Leveraging epidemiological surveillance data on several respiratory viruses, from Canada and the USA, from 2016 to 2023, we found that whereas in the pre-COVID-19 pandemic period, weather had a strong effect, in the pandemic period the effect of weather was strongly reduced and mobility played a more relevant role. These results, together with previous studies, indicate that behavioral changes resulting from the non-pharmacological interventions implemented to control SARS-CoV2, interfered with the dynamics of other respiratory viruses, and that the past dynamical equilibrium was disturbed, and perhaps permanently altered, by the COVID-19 pandemic.

The effect of social movements on COVID-19 case increases and death in Turkey

Objectives

This study aimed to determine whether there is a correlation between COVID-19 cases and deaths because of COVID-19 and community movements in Turkey and to develop a strategy for future outbreaks.

Methods

The study's data covers COVID-19 cases and deaths between March 11, 2020, and December 16, 2021, and Turkey's Google community movements between these dates. The COVID-19 cases and deaths were obtained from Turkey's Ministry of Health COVID-19 Information Platform. Community mobility collated by Google is retail and recreation, supermarket and pharmacy, parks, public transport, workplaces visits, and residential. The data were transferred via "SPSS (Statistical Package for Social Sciences) for Windows 25.0 (SPSS Inc, Chicago, IL" and statistical analysis was performed. The Spearman correlation test was used as a statistical method. In the Kruskal-Wallis Test, categorical variables were created using increases and decreases in community movements based on the baseline.

Results

A weak positive correlation between daily COVID-19 deaths and supermarket and pharmacy activity (r = 0.28 p < 0.01). A weak negative correlation with park activity (r = -0.23 p < 0.01). A weakly positive and significant relationship with workplace visits mobility (r = 0.10 p < 0.05). There was a weak positive significant relationship with public transport mobility (r = 0.10 p < 0.01), including a weak positive significant relationship with residential (r = 0.12 p < 0.01).

Conclusions

Social distancing measures (such as reducing community mobility) and educating people on viral transmission in possible epidemics will save us time developing new diagnostic tests and vaccine studies.