1. Introduction
The COVID-19 outbreak was first reported in China in 2019 [
1,
2] and spread worldwide in early 2020. Japan declared a state of emergency in seven (of 47) prefectures on 7 April 2020 and extended it to all prefectures on 13 April 2020. The state of emergency was withdrawn on 25 May 2020. During this state of emergency, unlike many other countries where city lockdowns were enforced, in Japan, citizens self-isolated. The mortality rate (per population) in Japan is relatively low compared to the global rate; the total number of confirmed deaths in Japan is 846 (25 May 2020), corresponding to 6.72 per million people [
3]. Although a straightforward comparison is infeasible, this number is smaller than that of many other countries with the same order of magnitude of population: 541, 504, 435, and 295 in Italy, the United Kingdom, France, and the United States, respectively, but larger than 5.18 and 4.0 in Indonesia and Australia, respectively (25 July 2020).
An additional difficulty in understanding the morbidity rate is the unreliability of the diagnosis of COVID-19. The number of polymerase chain reaction (PCR) tests, a simple and cost-effective method, is limited in Japan, partly because of its reliability. Therefore, chest CT is used for a fast-track, highly accurate diagnosis [
4]. Some patients do not exhibit any common pandemic symptoms [
5], thereby complicating morbidity rate assessment in different areas (e.g., population composition).
Morbidity and mortality statistics have been updated every day in each prefecture in Japan, which provides a good opportunity for local studies. A notable feature of Japan is that no medical collapse has been reported. In addition, due to the health insurance system, free medical care for COVID-19 has been offered. Thus, the reliability of the mortality rate is more accurate than that of the morbidity rate, because patients with weak or mild symptoms may not be tested. However, to avoid nosocomial infections and medical resource shortages, it was suggested that people with specific symptoms (e.g., fever with temperature >37.5 °C for no more than four consecutive days) stay home and avoid seeking medical attention, unless they had been in close contact with an infected person(s) or had recently visited foreign countries. Such a policy may result in longer latency in the reported cases.
In general, coronaviruses are considered to spread mainly by respiratory droplets and contact via droplets [
6]. Droplets tend to fall to the ground close to the infected host. Droplet transmission is typically limited to short distances, generally less than 2 m. There exist some hypotheses about transmission due to airborne transmission that remain in flight for one hour or longer [
7]. For both mechanisms, the ambient condition potentially influences the duration of droplet and airborne spread. Several co-factors potentially influence COVID-19 morbidity/mortality rates [
8,
9,
10,
11,
12,
13,
14]); among them, ambient conditions have been considered here.
The effect of ambient temperature on the mortality was discussed in Wuhan [
8]. Positive and negative associations were found between daily COVID-19 death counts and daily temperature difference and absolute humidity, respectively. The effect of high temperature and humidity on the transmission of COVID-19 was discussed using relative humidity as a measure [
9]. Their finding suggested that high temperature and humidity may suppress COVID transmission. Furthermore, the effect of weather on COVID-19 cases employing a case in Jakarta was presented in [
10]. They reported that only average temperature is correlated with the pandemic spread. The effect of ambient temperature on the confirmed cases was discussed in more than 100 Chinese cities, and it was concluded that there is no evidence supporting that COVID-19 case counts would decline when the weather becomes warmer [
12]. The effect of ambient temperature and absolute humidity on the confirmed cases was investigated in cities in China, and the researchers commented that the epidemic might gradually ease partially due to rising temperatures [
13]. Instead, no correlation with UV and temperature on the transmission of COVID-19 was reported in [
15].
Following Chinese studies, case studies in different countries have been reported. Briz-Redón and Serrano-Aroca [
16] evaluated the spatiotemporal analysis of temperature in the cases of early COVID-19 evolution in Spain. Pirouz et al. [
17] discussed the correlation between daily confirmed cases and temperature, humidity, and velocity with multivariate analysis in Italy. Application of neural networks for its estimation is also discussed in [
18]. A similar attempt has been made in Oslo, Norway [
19]. Recent studies have confirmed the effect of temperature and relative humidity on morbidity rates in Brazil [
20,
21]. From these studies, it is difficult to derive a consistent conclusion on the effect of the weather on the spread of COVID-19. Studies of influenza suggested the importance of ambient conditions for its spread: lower spread for higher humidity (e.g., [
22,
23]).
Studies with wider scopes included global data analysis, discussing how temperature and humidity are correlated with the infection and fatality rates of the COVID-19 pandemic [
24,
25]. The region of interest is wide (country level) in these studies, and thus it is not directly applicable to the ruling or regulation. In addition, some modeling studies have been proposed. However, parameter setting is not easy for this type of novel virus spread [
14,
26]; in most modeling studies, the parameters relating to the weather or population cannot be given explicitly. Instead, the effect of population density on the spreading effect of the epidemic has been discussed under some assumptions [
27].
Nevertheless, none of the aforementioned research and modeling studies simultaneously considered the impact of population density and ambient conditions. A question that arises here is To what extent do ambient conditions and population density influence morbidity and mortality rates in different cities? Unlike the aforementioned studies, a major feature of Japan is the relative homogeneity of the health insurance and care system without medical collapse during this pandemic. In addition, the difference in household wealth is relatively small in Japan [
28]. The average annual salary per population is USD 34,400 to 39,900 (USD 1 = JPY 107). The standard deviation of household consumption in each prefecture is 10% or less [
28]. With all these demographic factors, the data sample discussed here provides a convenient case study with less bias. In a recent study [
29], we examined the time course of the morbidity rate of different prefectures in Japan and found that the durations of the spread and decay stages can be characterized by population density, temperature, and absolute humidity. An additional factor would be the ratio of the elderly to the entire population; in Japan, this ratio reached 28.4% [
30], which is ranked the highest globally.
This novel study aimed to evaluate the effect of ambient temperature and humidity on mortality and morbidity rates in different prefectures in Japan. Additionally, it considered the influence of population density and composition. To the best of the authors’ knowledge, this is the first study to highlight the environmental factors’ effect during COVID-19 in Japan. The model of Japan provides an interesting case study for different factors, as the medical service and social reaction is almost uniform nationwide and high-quality data were recorded properly. If the correlation of the pandemic with population density and ambient conditions is significant, the findings will be useful to set the level and duration for a strict lockdown period for each city considering the environmental factors and in planning future pandemic measures.
The organization of this study is as follows. In
Section 2, the data sources of COVID-19 in Japan and weather data are mentioned. Then, the statistical method for data processing is explained briefly. In
Section 3, effect of population density, elderly population, and ambient conditions on the morbidity/mortality rates are evaluated statistically. Based on the evaluation, multivariate linear regression has been conducted to estimate the morbidity/mortality rate from these parameters. In
Section 4, provides discussion of the results including the limitation. The conclusion is given in
Section 5.
4. Discussion
In this study, we analyzed the morbidity and mortality rates in different prefectures in Japan, where the number of confirmed deaths and daily confirmed positive counts were higher than 4 and 10, respectively. A major feature of Japan was the relative homogeneity of the health insurance and care system without medical collapse during this pandemic, in addition to household wealth. The Japanese strategy included identifying infection clusters at an early stage, to the best possible extent. However, the criteria for conducting tests (diagnosis) on potential patients may not be uniform in different prefectures; some patients may exhibit weak symptoms. Thus, after retracting the state of emergency on 25 May 2020, we processed the data for morbidity and mortality rates in 14 prefectures.
The morbidity/mortality rates were then shown to be proportional to the population density. In previous studies, this factor was not considered [
12] nor was correlation between different cities considered [
17]. After excluding the number of confirmed deaths in cluster infections related to hospital and care services, we observed modest correlation among different cities in terms of population density. It is worth noting that no strict closure was applied in Japan. Next, we found a good correlation between population density and the spread of COVID-19. This finding implicitly represents social distancing. In Tokyo and Osaka, which are considered among cities with the highest population densities worldwide, infection is potentially more likely to occur compared to other less dense regions. However, this may not be the case reported in other countries where strict lockdown was implemented. In Wuhan (China), the duration of the decaying stage was only 10 days, with almost no contact during the period. However, such strict lockdown may not be allowed in most countries to avoid severe social and economic damage. Therefore, this study demonstrates that population density should be considered for avoiding potential spread in future pandemics. Moreover, this finding may be useful to improve the simulation model of epidemic transmission [
37,
38]. The maximum temperature and absolute humidity differences were the dominant ambient factors characterizing morbidity and mortality rates. As shown in
Figure 4, cases and deaths in Ishikawa and Toyama prefectures have a different tendency than that in other prefectures—as COVID-19 occurred in a very limited area in these prefectures. In general, for higher temperature and absolute humidity, the morbidity and mortality rates were decreased. For example, the population density of Hyogo (650.4 capita/km
2) is nearly equal to that of Okinawa (637.5 capita/km
2). However, the total cases in Hyogo were 8.6 times that of Okinawa. The daily maximum temperature in Hyogo was 7 °C lower than in Okinawa. This relationship can be observed in other prefectures but not all due to mild correlation with weather condition. The reason for higher correlation with absolute humidity difference is unclear. However, one potential reason would be the relatively small variation in a limited period (from mid-March to mid-May). Further study of key factors would be needed. The ambient conditions in Okinawa prefecture differ the most from those of other prefectures in Japan. If the data of Okinawa are excluded, the correlation of confirmed cases and deaths improved. In particular, the total cases and deaths normalized by population have a mild correlation with the maximum temperature and absolute humidity averaged over spread duration (from 0.13 <
R2 < 0.18 to 0.37 <
R2 < 0.55).
The effect of ambient conditions on the morbidity and mortality rates was shown to be modest over multiple prefecture studies. As mentioned in the introduction, this was a controversial COVID-19 issue. Our study hypothesized that this may be caused by population density, which was not considered in previous studies, as well as the uniformity of the policy, health insurance system, household wealth, etc.
The morbidity and mortality rates were roughly derived via multivariate analysis. Note that the ambient parameters are cross-correlated with each other, and thus further research and investigation are needed. Their adjusted-R2 was almost the same; 0.69 (p < 0.01) for positive cases, and those for confirmed deaths including and excluding nosocomial infection were 0.53 (p < 0.05) and 0.15 (p = 0.25), respectively. This statistical finding may be improved for modeling studies. The correlation with the mortality rate excluding nosocomial infection was relatively low, suggesting that nosocomial infection would be a part of COVID-19 transmission at least in Japan.
Unlike previous studies that discussed the correlation with ambient condition in each city (e.g., [
17]), our study explores common factors over 14 prefectures, resulting in lower
p-value as compared to such studies. In such cases, the uncertainty of measured ambient condition would also be another factor to influence the correlation. For example, no correlation with ambient condition was observed in the analysis of 122 cities in China [
12].
Note that according to the record of the Ministry of Health in Japan, no pandemic has been reported in the last 50 years [
39]. Thus, a comparison with other epidemics is infeasible. Common influenza has been recorded, but only at fixed points (hospitals), making proper comparison difficult [
39]. However, the finding of this study that presents the effect of population density and ambient conditions may be useful when considering measures for potential future pandemics.