key: cord-0984834-8qgs5v25
authors: Menebo, Mesay Moges
title: Temperature and precipitation associate with Covid-19 new daily cases: A correlation study between weather and Covid-19 pandemic in Oslo, Norway
date: 2020-05-29
journal: Sci Total Environ
DOI: 10.1016/j.scitotenv.2020.139659
sha: 194abb22ae1cab8a1be6267e3fff7f82a27bcc5b
doc_id: 984834
cord_uid: 8qgs5v25

Abstract This study aims to analyze the correlation between weather and covid-19 pandemic in the capital city of Norway, Oslo. This study employed a secondary data analysis of covid-19 surveillance data from the Norwegian public health institute and weather data from the Norwegian Meteorological institute. The components of weather include minimum temperature (°C), maximum temperature (°C), temperature average (°C), normal temperature (°C), precipitation level (mm) and wind speed (m/s). Since normality was not fulfilled, a non-parametric correlation test was used for data analysis. Maximum temperature (r = 0.347; p = .005), normal temperature(r = 0.293; p = .019), and precipitation level (r = −0.285; p = .022) were significantly correlated with covid-19 pandemic. The finding serves as an input to a strategy making against the prevention of covid-19 as the country prepare to enter into a new weather season.

The Chinese government announced a new type of pneumonia, coronavirus (covid-19) , with an obscured etiology on January 7, 2020 (WHO, 2020a). The rapid spread severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the declaration of a global pandemic barely three months after its emergence. The infection included symptomatic or asymptomatic manifestations where 80% of infections are mild (no pneumonia manifestations) or asymptomatic (WHO, 2020b) . Most people infected experience mild to moderate respiratory illness such as fever, coughing and shortness of breath. Most estimates of the incubation period for Covid-19 range from 1-14 days, most commonly around five days (Linton et al., 2020) . In more severe cases, infection can cause pneumonia, severe acute respiratory syndrome, kidney failure and even death (Cheng et al., 2020) . Fever (< 39.1 °C) was the most frequent symptom, and Cough is the second most common symptom observed (Gu et al., 2020) . Other symptoms reported include headache, rhinorrhea, gastrointestinal symptoms, sore throat, and fatigue (Michelen et al., 2020) .

Norway have reported the first confirmed case of covid-19 on February 26, 2020 (FHI, 2020).

As of May 3, 2020, it has increased to 7809 total confirmed cases, which means a 1.47 infection per 1000. The capital city Oslo has registered the highest number of cases (2399, 3.46/1000) followed by other provinces nearby like Viken (2088, 1.68/1000)(FHI, 2020).

J o u r n a l P r e -p r o o f have also considered certain climatic conditions as predisposing factors (D'Amato et al., 2014) .

Accordingly, climate components like temperature, rainfall and wind speed might in this specific case of covid-19 be biological catalysts for the interaction between covid-19 and humans. Virus transmission is affected by a number of factors, including host behavior, host defense mechanisms, and virus infectivity (Cory, 2015) , population density (Brown et al., 2008) and environmental determinants. Uncertainties remain with respect to the relative importance of environmental factors and the roles that they play on covid-19. Especially little is known in Nordic countries that have four different weather seasons where the variability of weather change between days is large.

Hence, this research will contribute to efforts against covid-19 as countries enter into new weather seasons and align their lock-down strategy measures accordingly.

Oslo, the capital of Norway, sits on the country's southern coast at the head of the 

A secondary data on daily cases of covid-19 with 64 (N) observations was obtained from the Norwegian public health institute database and weather data from the Norwegian Meteorological institute for the period February 27-May 2, 2020. The components of weather J o u r n a l P r e -p r o o f include minimum temperature (°C), maximum temperature (°C), temperature average (°C), normal temperature (°C), precipitation level (mm) and wind speed (m/s).

The basic assumption of Pearson's correlation for the variable 'number of daily new cases of covid-19' was not fulfilled (Shapiro-wilk test of normality, p<.0001). Hence, the non-parametric correlation estimation (Spearman correlation coefficient) was used to examine and determine the relationships between covid-19 pandemic and weather components. A bivariate, two-tailed analysis at 95% confidence interval was applied.

Covid-19 new cases escalated very rapidly in Norway, especially during day 25 to day 40 (See Figure 1(a) ). In the first 12 days after first confirmation of the case, the change in increase of new daily cases was near to zero. Since day 13, the daily reported new cases started to triple until it gradually came to a low daily increase on day 61. Descriptive overview of the weather data indicates (See Table 1 ) a minimum temperature of -7.5 °C and the highest maximum temperature of 21.9 °C during the study period. In addition to that, 0mm was the lowest recorded rainfall while 16.4mm was the highest. The lowest wind speed recorded on day 8 with value of 1.3m/s and highest wind speed of 6.6 m/s on day 47.

Among the seven weather variables, maximum temperature (r= 0.347; p=005), and normal temperature (r= 0.293; p=.019) were positively and significantly correlated with covid-19. In the contrary, precipitation that was measured at 7 am was negatively and significantly correlated with J o u r n a l P r e -p r o o f covid-19. However, the other weather variables(Minimum temperature, Average temperature, wind speed, highest wind speed) were not significantly correlated with covid-19.

In this study, the pattern of climate change provides a picture of the occurrence of covid-19 in Oslo. We found a preliminary evidence to state that high temperature (normal and maximum) and low precipitation (rainfall) level associate with positive incidence of Covid-19. This association is in line with previous research that shows the impact of temperature, dry weather and precipitation on human west Nile virus infections (Ruiz et al., 2010; G. Wang et al., 2010) . A variety of arguments can be given for the positive relationship between temperature and new cases. One could be a hypothesis that people are more prone to break lock-down 'stay-home' rules when the sun is shining outside, so eventually become exposed to the virus. In the contrary, the negative relationship between precipitation and new cases is the reverse: whereby people avoid coming out if it is rainy.

The implications in this study oppose with a recent finding from china by J. Wang et al. (2020) where their study claimed that high temperature and high humidity reduce the transmission of Covid-19. To answer the question of 'when or where is it/is not weather important on impacting covid-19 incidence?' requires an investigation of a moderating variable of the effect.

In spite of the weather, it is believed that factors like high mobility, population density, household condition contribute much to new covid-19 in big cities like Oslo. As the capital city of Norway, Oslo is the main economic destination for job seekers who come from various regions, making it the fastest growing major city in Europe at the time. Oslo's population density J o u r n a l P r e -p r o o f is also very high and this allows covid-19 transmission to be very fast. Oslo's municipality has an average of 1,400 people per square kilometer, outpaced by the population density of Oslo's total urbanized area which is about 1,500 people per square kilometer once its surrounding boroughs are factored in (Metrics, 2020) .With population density like this allows covid-19 to spread rapidly (Kuchler et al., 2020) .

This study brings new insight on the relationship of precipitation, temperature and new covid-19 cases, and could assist local policymaking. However, it still has its own limitations:

Primarily, the impact of other key factors-, which might have greatly influenced the core relationship like lock-down implementation, testing capacities, development of herd immunities, sanitization attitudes were not accounted. Secondly, correlational analysis assumes linear relationship between two variables that correlates. In this study, testing dates (daily corona cases) might not exactly imply to infection dates (or virus transmission dates). It could be possible that the daily corona case reported on the testing dates has same infection date or a lag of incubation periods. Therefore, which date's weather information data to grab to match with the daily new cases is ill-defined and another limitation of this study. As an intervention to this problem, three additional correlational relationships (See table 3) assuming weather information data from 5, 6 or 14 days (Covid-19 incubation periods) earlier to the dates the corona daily cases were reported is also conducted and compared with the main correlational analysis. The relationship significance between normal temperature ( o C) and daily new corona cases is consistent in all the four scenarios, while the relationship significance between precipitation (mm) and daily new corona cases is consistent in the two scenarios.

Journal Pre-proof J o u r n a l P r e -p r o o f

Temperature and precipitation are an important factor in determining the incidence rate of daily covid-19 cases in Oslo. Maximum temperature and normal temperature are positively associated with covid-19. Whereas precipitation is associated negatively. One hypothesis could be that rainfall (vs sunny weather) boosts the 'stay-home' rules. Highest wind speed (m/s) 2.2 10.3 5.9 Table 2 Relationship between the number of daily new cases of covid-19 and weather components ** Correlation is significant at the 0.01 level * Correlation is significant at the 0.05 level. J o u r n a l P r e -p r o o f Note; Assumptions of matching Test date-Daily new cases-Infection date-Weather information were drawn before the correlational analysis. Findings on the first column are based on an assumption that testing was conducted without requirement of any symptoms as a pre-requisite for testing (e.g. a public mass testing campaigns). In this case, it could be that infection date and testing date are the same. Therefore weather information of the test date were considered in the analysis. Second is, an incubation period of days were considered since the Norwegian institute of public health at some point had some recommendations that testing should be conducted for people only with acute respiratory tract infection symptoms (e.g. fever, cough or breathing difficulties, or who a doctor suspects has COVID-19). In this case, infection date was considered 5, 6 or 14 days (corona incubation periods, 5-6 are common) before test date. Accordingly, weather information 5 days, 6 days or 14 days earlier to the testing date were considered in the correlation analysis column 2, column 3 or column 5 respectively. Figure 1 

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