ABSTRACT
This study integrated dynamic models and statistical methods to design a novel macroanalysis approach to judge the climate impacts. First, the incidence difference across Köppen-Geiger climate regions was used to determine the four risk areas. Then, the effective influence of climate factors was proved according to the non-climate factors' non-difference among the risk areas, multi-source non-major component data assisting the proof. It is found that cold steppe arid climates and wet temperate climates are more likely to transmit SARS-CoV-2 among human beings. Although the results verified that the global optimum temperature was around 10 °C, and the average humidity was 71%, there was evident heterogeneity among different climate risk areas. The first-grade and fourth-grade risk regions in the Northern Hemisphere and fourth-grade risk regions in the Southern Hemisphere are more sensitive to temperature. However, the third-grade risk region in the Southern Hemisphere is more sensitive to relative humidity. The Southern Hemisphere's third-grade and fourth-grade risk regions are more sensitive to precipitation.
Subject(s)
COVID-19 , Humans , Pandemics , SARS-CoV-2 , Climate , TemperatureABSTRACT
More than 30 months into the novel coronavirus 2019 (COVID-19) pandemic, efforts to bring this prevalence under control have achieved tentative achievements in China. However, the continuing increase in confirmed cases worldwide and the novel variants imply a severe risk of imported viruses. High-intensity non-pharmaceutical interventions (NPIs) are the mainly used measures of China's early response to COVID-19, which enabled effective control in the first wave of the epidemic. However, their efficiency is relatively low across China at the current stage. Therefore, this study focuses on whether measurable meteorological variables be found through global data to learn more about COVID-19 and explore flexible controls. This study first examines the control measures, such as NPIs and vaccination, on COVID-19 transmission across 189 countries, especially in China. Subsequently, we estimate the association between meteorological factors and time-varying reproduction numbers based on the global data by meta-population epidemic model, eliminating the aforementioned anthropogenic factors. According to this study, we find that the basic reproduction number of COVID-19 transmission varied wildly among Köppen-Geiger climate classifications, which is of great significance for the flexible adjustment of China's control protocols. We obtain that in southeast China, Köppen-Geiger climate sub-classifications, Cwb, Cfa, and Cfb, are more likely to spread COVID-19. In August, the RSIM of Cwb climate subclassification is about three times that of Dwc in April, which implies that the intensity of control efforts in different sub-regions may differ three times under the same imported risk. However, BSk and BWk, the most widely distributed in northwest China, have smaller basic reproduction numbers than Cfa, distributed in southeast coastal areas. It indicates that northwest China's control intensity could be appropriately weaker than southeast China under the same prevention objectives.
ABSTRACT
More than 30 months into the novel coronavirus 2019 (COVID-19) pandemic, efforts to bring this prevalence under control have achieved tentative achievements in China. However, the continuing increase in confirmed cases worldwide and the novel variants imply a severe risk of imported viruses. High-intensity non-pharmaceutical interventions (NPIs) are the mainly used measures of China's early response to COVID-19, which enabled effective control in the first wave of the epidemic. However, their efficiency is relatively low across China at the current stage. Therefore, this study focuses on whether measurable meteorological variables be found through global data to learn more about COVID-19 and explore flexible controls. This study first examines the control measures, such as NPIs and vaccination, on COVID-19 transmission across 189 countries, especially in China. Subsequently, we estimate the association between meteorological factors and time-varying reproduction numbers based on the global data by meta-population epidemic model, eliminating the aforementioned anthropogenic factors. According to this study, we find that the basic reproduction number of COVID-19 transmission varied wildly among Köppen-Geiger climate classifications, which is of great significance for the flexible adjustment of China's control protocols. We obtain that in southeast China, Köppen-Geiger climate sub-classifications, Cwb, Cfa, and Cfb, are more likely to spread COVID-19. In August, the RSIM of Cwb climate subclassification is about three times that of Dwc in April, which implies that the intensity of control efforts in different sub-regions may differ three times under the same imported risk. However, BSk and BWk, the most widely distributed in northwest China, have smaller basic reproduction numbers than Cfa, distributed in southeast coastal areas. It indicates that northwest China's control intensity could be appropriately weaker than southeast China under the same prevention objectives.
ABSTRACT
The coronavirus disease 2019 (COVID-19) pandemic is a global crisis, and medical systems in many countries are overwhelmed with supply shortages and increasing demands to treat patients due to the surge in cases and severe illnesses. This study aimed to assess COVID-19-related essential clinical resource demands in China, based on different scenarios involving COVID-19 spreads and interventions. We used a susceptible-exposed-infectious-hospitalized/isolated-removed (SEIHR) transmission dynamics model to estimate the number of COVID-19 infections and hospitalizations with corresponding essential healthcare resources needed. We found that, under strict non-pharmaceutical interventions (NPIs) or mass vaccination of the population, China would be able to contain community transmission and local outbreaks rapidly. However, under scenarios involving a low intensity of implemented NPIs and a small proportion of the population vaccinated, the use of a peacetime-wartime transition model would be needed for medical source stockpiles and preparations to ensure a normal functioning healthcare system. The implementation of COVID-19 vaccines and NPIs in different periods can influence the transmission of COVID-19 and subsequently affect the demand for clinical diagnosis and treatment. An increased proportion of asymptomatic infections in simulations will not reduce the demand for medical resources; however, attention must be paid to the increasing difficulty in containing COVID-19 transmission due to asymptomatic cases. This study provides evidence for emergency preparations and the adjustment of prevention and control strategies during the COVID-19 pandemic. It also provides guidance for essential healthcare investment and resource allocation.