Correlation between COVID-19 Morbidity and Mortality Rates in Japan and Local Population Density, Temperature, and Absolute Humidity

Abstract

This study analyzed the morbidity and mortality rates of the coronavirus disease (COVID-19) pandemic in different prefectures of Japan. Under the constraint that daily maximum confirmed deaths and daily maximum cases should exceed 4 and 10, respectively, 14 prefectures were included, and cofactors affecting the morbidity and mortality rates were evaluated. In particular, the number of confirmed deaths was assessed, excluding cases of nosocomial infections and nursing home patients. The correlations between the morbidity and mortality rates and population density were statistically significant (p-value < 0.05). In addition, the percentage of elderly population was also found to be non-negligible. Among weather parameters, the maximum temperature and absolute humidity averaged over the duration were found to be in modest correlation with the morbidity and mortality rates. Lower morbidity and mortality rates were observed for higher temperature and absolute humidity. Multivariate linear regression considering these factors showed that the adjusted determination coefficient for the confirmed cases was 0.693 in terms of population density, elderly percentage, and maximum absolute humidity (p-value < 0.01). These findings could be useful for intervention planning during future pandemics, including a potential second COVID-19 outbreak.

Raiders of the Lost Correlation: A Guide on Using Pearson and Spearman Coefficients to Detect Hidden Correlations in Medical Sciences

“…Some plausible scenarios in which it is legitimate to expect hidden causal correlations are: i) the effects of air pollution, where exceeding specific thresholds can cause an increase in population mortality and disease due to an impairment of the immune and respiratory systems [ 5 , 18 ], ii) the levels of interest, stress, and anxiety among the population, which can reach high values ​​when negative news exceeds a particular number [ 7 ], iii) in the specific case of the novel coronavirus, exceeding a certain value of the population density could have an important role in increasing the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [ 6 , 18 , 20 ], and iv) particulate matter could act as a viruses-carrier, especially beyond specific thresholds [ 17 - 18 ].…”

“…The search for statistical correlations between two data distributions constitutes one of the fundamental elements of scientific research [1][2][3][4]. Particularly in the fields of public health, social sciences, infoveillance, and epidemiology, these can provide important information on risk perception and the spread of viruses and bacteria [5][6][7][8]. The two most frequently used correlation indices are those of Pearson and Spearman: the first one measures the linear relationship between two continuous random variables and is adopted when the data follows a normal distribution while the second one measures any monotonic relationship between two continuous random variables and is adopted when the data do not follow a normal distribution; both range from -1 to 1 [1][2][3][4].…”

Early Phase Management of the SARS-CoV-2 Pandemic in the Geographic Area of the Veneto Region, in One of the World’s Oldest Populations

“…High levels of pollution, especially PM 10 and PM 2.5 , were previously found to be related to a high incidence of COVID-19 cases [10,11]. Furthermore, the climate and wind speed are supposedly related to increases in COVID-19 cases, due to several pieces of evidence [12][13][14].…”

“…The absence of a correlation between cases and the population density is surprising. In many studies conducted since the beginning of the SARS-CoV-2 outbreak, the population density has been considered to be related to the number of cases, mainly because a high population density can facilitate the spread of the virus [14][15][16][17][18]. However, there are also other studies that did not find a correlation between the density and cases [12,[19][20][21][22].…”

One Biosecurity: a unified concept to integrate human, animal, plant, and environmental health

“…Urban areas with high human population density appear to have been hardest hit by the SARS- CoV-2 pandemic [ 47 ]. It is expected that the urbanisation of rural regions and the increasing growth of megacities is likely to lead to a heightened risk of pest and disease incursions as well as the emergence of new zoonoses due to the increasing interaction of urban communities with invasive alien species.…”