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Spatio-temporal variations in COVID-19 in relation to the global climate distribution and fluctuations.

ABSTRACT: This study investigated the spatio-temporal variations in the occurrence of COVID-19 (confirmed cases and deaths) in relation to climate fluctuations in 61 countries, scattered around the world, from December 31, 2019 to May 28, 2020. Logarithm transformation of the count variable (COVID-19 cases) was used in a multiple linear regression model to predict the potential effects of weather variables on the prevalence of the disease. The study revealed strong associations (-0.510 ≤ r ≤ -0.967; 0.519 ≤ r ≤ 0.999) between climatic variables and confirmed cases of COVID-19 in majority (68.85%) of the selected countries. It showed evidences of 1 to 7-day delays in the response of the infection to changes in weather pattern. Model simulations suggested that a unit fall in temperature and humidity could increase (0.04-18.70%) the infection in 19.67% and 16.39% of the countries, respectively, while a general reduction (-0.05 to 9.40%) in infection cases was projected in 14.75% countries with a unit drop in precipitation. In conclusion, the study suggests that effective public health interventions are crucial to containing the projected upsurge in COVID-19 cases during both cold and warm seasons in the southern and northern hemispheres.

SUBMITTER: Matthew OJ

PROVIDER: S-EPMC7973081 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Spatio-temporal variations in COVID-19 in relation to the global climate distribution and fluctuations.

Matthew Olaniran Jonathan OJ Eludoyin Adebayo Oluwole AO Oluwadiya Kehinde Sunday KS

Spatial and spatio-temporal epidemiology 20210319

This study investigated the spatio-temporal variations in the occurrence of COVID-19 (confirmed cases and deaths) in relation to climate fluctuations in 61 countries, scattered around the world, from December 31, 2019 to May 28, 2020. Logarithm transformation of the count variable (COVID-19 cases) was used in a multiple linear regression model to predict the potential effects of weather variables on the prevalence of the disease. The study revealed strong associations (-0.510 ≤ r ≤ -0.967; 0.519 ...[more]

PMID: 33980401

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Project description:BackgroundThe coronavirus disease 2019 (COVID-19) has become a pandemic. Few studies have been conducted to investigate the spatio-temporal distribution of COVID-19 on nationwide city-level in China.ObjectiveTo analyze and visualize the spatiotemporal distribution characteristics and clustering pattern of COVID-19 cases from 362 cities of 31 provinces, municipalities and autonomous regions in mainland China.MethodsA spatiotemporal statistical analysis of COVID-19 cases was carried out by collecting the confirmed COVID-19 cases in mainland China from January 10, 2020 to October 5, 2020. Methods including statistical charts, hotspot analysis, spatial autocorrelation, and Poisson space-time scan statistic were conducted.ResultsThe high incidence stage of China's COVID-19 epidemic was from January 17 to February 9, 2020 with daily increase rate greater than 7.5%. The hot spot analysis suggested that the cities including Wuhan, Huangshi, Ezhou, Xiaogan, Jingzhou, Huanggang, Xianning, and Xiantao, were the hot spots with statistical significance. Spatial autocorrelation analysis indicated a moderately correlated pattern of spatial clustering of COVID-19 cases across China in the early phase, with Moran's I statistic reaching maximum value on January 31, at 0.235 (Z = 12.344, P = 0.001), but the spatial correlation gradually decreased later and showed a discrete trend to a random distribution. Considering both space and time, 19 statistically significant clusters were identified. 63.16% of the clusters occurred from January to February. Larger clusters were located in central and southern China. The most likely cluster (RR = 845.01, P < 0.01) included 6 cities in Hubei province with Wuhan as the centre. Overall, the clusters with larger coverage were in the early stage of the epidemic, while it changed to only gather in a specific city in the later period. The pattern and scope of clusters changed and reduced over time in China.ConclusionsSpatio-temporal cluster detection plays a vital role in the exploration of epidemic evolution and early warning of disease outbreaks and recurrences. This study can provide scientific reference for the allocation of medical resources and monitoring potential rebound of the COVID-19 epidemic in China.

Project description:BACKGROUND:The licensed doctor misdistribution is one of the major challenges faced by China. However, this subject remains underexplored as spatial distribution characteristics (such as spatial clustering patterns) have not been fully mapped out by existing studies. To fill the void, this study aims to explore the spatio-temporal dynamics and spatial clustering patterns of different subtypes of licensed doctors (i.e., clinicians, traditional Chinese medicine doctors, dentists, public health doctors, general practitioners) in China. METHODS:Data on the licensed doctor quantity and population during 2012-2016 was obtained from the National Health (and Family Planning) Yearbook. Functional boxplots were used to visualize and compare the temporal trends of densities of different subtypes of licensed doctors. This study adopted two complementary spatial statistics (space-time scan statistics and Moran's I statistics) to explore the spatio-temporal dynamics and spatial clustering patterns of licensed doctor distribution in China. The former was used to explore the spatial variations in the temporal trends of licensed doctor density during 2012-2016, and the latter was adopted to explore the spatial changing patterns of licensed doctor distribution during the research period. RESULTS:The results show that the densities of almost all subtypes of licensed doctors displayed upward trends during 2012-2016, though some provincial units were left behind. Besides, spatial distribution characteristics varied across different subtypes of licensed doctors, with the low-low cluster area of general practitioners being the largest. CONCLUSIONS:The misdistribution of licensed doctors is a global problem and China is no exception. In order to achieve a balanced distribution of licensed doctors, the government is suggested to introduce a series of measures, such as deliberative policy design and effective human resource management initiatives to educate, recruit, and retain licensed doctors and prevent a brain drain of licensed doctors from disadvantaged units.

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Spatio-temporal variations in COVID-19 in relation to the global climate distribution and fluctuations.

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Spatio-temporal variations in COVID-19 in relation to the global climate distribution and fluctuations.

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