key: cord-0850596-1dq91x2r
authors: Mandal, Chandi C.; Panwar, M. S.
title: Can the summer temperature drop COVID-19 cases?
date: 2020-06-07
journal: Public Health
DOI: 10.1016/j.puhe.2020.05.065
sha: 0bbfbaaf1a302da40204fe62e192ac839218b0e9
doc_id: 850596
cord_uid: 1dq91x2r

Abstract Objective In spite of huge global, national and local preventive measures including travel restriction, social distancing and quarantines, outbreak of novel coronavirus SARS-CoV-2 develops COVID-19 worldwide pandemic. SARS-CoV-2 emerging from Wuhan, China took only three months to cover > 200 countries worldwide by infecting more than 2.4 million people and killing more than 150000 people. Though, this infection at the early stage creates seasonal flu-like symptoms with a higher illness, it eventually causes a higher mortality. Epidemiological studies not only find the causes of many health issues, but also suggest preventive measures. This study aimed to see the link between environment temperature and COVID-19 cases. Study design The monthly average environment temperature (MAET) and various COVID-19 cases of a country were collected, and analyzed to see the relationship between these parameters. Methods Univariate analysis and statistical modeling were used to determine the relationship between environment temperature and different COVID-19 cases. Results This study found that the majorities of the countries having higher COVID-19 cases are located in the higher latitude (colder region) in the globe. As of 20th April data available, statistical analyses by various methods have found that strong negative correlations with statistical significance exist between MAET and several COVID-19 cases including total cases, active cases and cases per million of a country [Spearman correlation coefficients were -0.45, -0.42, and -0.50 for total cases, active cases and cases/per million, respectively]. Analysis by statistical log-linear regression model further supports that the chance of COVID-19 patients is fewer in warmer countries than in colder countries. Conclusion This pilot study proposes that cold environment may be an additional risk factor for COVID-19 cases.

Within three months from emerging of the novel corona virus at Wuhan city, China in December 2019, this pandemic outbreak has spread largely across the world. In February 12, WHO (world health organization) has named the disease caused by novel corona virus (2019-nCoV)as COVID-19 (Corona virus disease-2019).Virologists have named this virus as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The results of genome sequencing obtained from the infected patients revealed that this novel virus belongs to corona virus cluster and is closely related to other bat derived coronaviruses; bat-SL-CoVZC45 and bat-SL-CoVZXC21, but this new corona virus is somewhat related to SARS-CoV and MERS-CoV 1 . However, the origin of this virus is yet to be confirmed. In this stage, it may not be concluded that this virus has originated from animals (like Bats) or a chimeric virus 2, 3 . It might be the case that batcorona virus after specific mutations can gain the ability to affect human beings 3 .A few recent studies have found that COVID-19 can develop faster in old age individuals, but the infection rate may or may not vary with age, gender, ethnicities and races 4, 5 . In general, this novel corona virus infection develops symptoms like that of seasonal flu including fever, cough, expectoration, myalgia, sore throat and fatigue but the severity of the illness could be more than influenza. Some patients may develop shortness of breath, pneumonia, severe acute respiratory distress syndromes and multi organ failure 4, 6 . Accumulating evidences indicated that people having various diseases like diabetes, hypertension and cardiovascular diseases experience serious complication if they are infected with the SARS-CoV-2. The outbreak of the SARS-CoV-2 pandemic might be severely fatal than the most devastating influenza (Spanish flu) pandemic outbreak in 1918 that occurred a century ago, since almost 2.4 million COVID-19 confirmed cases with more than150,000 deaths have been reported from 200 countries by only three months of the evolving of this viral infection. Since seasonal flu is comparatively low in the summer as compared to winter, the common people along with various researchers think that the summer might drop the novel corona virus infection rate.

Thus, this current study was aimed to see the link between temperature and COVID-19 cases. The results from our statistical analysis suggested that negative relationship exists between various COVID-19 cases including total confirmed cases, active cases and cases per million of the countries with the monthly average environmental temperature. This study suggests that cold environment might be an additional risk factor for COVID-19 cases.

The data of COVID-19 cases for each country were collected throughout time from 25 th March -18 th April, 2020 from the website worldometers.info. The values of various COVID cases were highly dynamic and changed rapidly in every day. Thus, in this study, we had analysed separately the data of different dates, collected every six days intervals (25 th March, 30 th March,  6 th April, 12 th April and 18 th April, 2020) to see the consistency of the statistical results. In this study, we had included total cases and active cases because values of these two variables were there for all the countries throughout the study period, and are gradually increased by the changed values of every day. The absolute values of total and active cases may depend on population density. Thus, we have also considered population adjusted data i.e., cases/million. Monthly average environment temperature (MAET) was obtained by making the average of average highest and lowest temperature of a specific month of the capital of a country, collected from the website climatestotravel.com.

The locations of various countries were pointed in a world map by using a world map maker ArcGIS software described previously 7 . The top countries having at least 1000 coronavirus infection cases were marked in world map.

Univariate analysis: The Spearman and Kendall methods for univariate analysis were used to test the null hypothesis between two variables described previously 8, 9 . These methods provide the coefficient of correlation for observations which are not in linear relationship (mentioned as coefficient in tables). Both negative and positive correlations were denoted by negative and positive coefficients. Here, significant differences were considered for P<0.05. In this statistical analysis, we had included all data available in the website worldometers.info.

Statistical modeling: In this study, we have considered three response variables as following: (i) Cases per million (ii) Total cases and (iii) Active cases. By simple graphs it can be seen that there does not exist any linear relationship between temperature and COVID-19 considered variables. So we purpose a log-linear model to fit the COVID-19 cases data with temperature. A log-linear model can be defined by In this model, x i is independent variable denotes the average temperature of a month of the i th country whereas the response variable Y ij denotes the value of j th case for the i th country at the considered day. Here j will be 1, 2 and 3 for cases per million, total cases and active cases, respectively. In particular, we can write log-linear model for j=1, 2, 3 such as An objective of giving a log-linear model is to showing a trend in between temperature and other variables. It is expected that the outcomes from models will support our hypothesis under study based on univariate correlation coefficients.

Geographical distribution of countries having higher COVID-19 cases:

The outbreak of novel coronavirus (SARS-CoV-2) has first reported from Wuhan, China in the end of December 2019. In January 2020, a few individuals of other countries have also been infected with the coronavirus. However, at the mid of January 2020, number of confirmed COVID-19 cases became more than 200. By March 25, 2020, almost 200 countries have reported coronavirus infected cases, where thirty one countries were badly affected with more than 1000 confirmed cases. It was noticed that majority of the severely affected countries (27 out of 31) are geographically situated on the similar latitude of the Wuhan (30.59 0 N) or located towards the North Pole [ Figure 1 ]. Except Ecuador, none of these twenty countries belongs to the Equatorial zone. Three countries of the Southern hemisphere [Australia, Brazil, and Chile are located near by the tropic of Capricorn]. All these are relatively cold countries because of their geographical location. It was noticed that Hong Kong (22.31 0 N) and Taiwan (23.69 0 N), the territory/country neighboring China which are located away from Wuhan towards the equatorial side, had reported at least 10 COVID-19 confirmed cases by the end of January 2020. But till 25 th March, 2020, the infected individuals were less than 500 in these two countries. Besides many reasons for spreading infection, cold environmental temperature could be an underestimated additional risk factor for COVID-19 cases.

The above initial observations had attracted our attention to see the link between environment temperature and the novel coronavirus infection cases of a country. Firstly, univariate analysis was done between monthly average environment temperature (MAET) and coronavirus infected cases of a country, taking all the available data present in the website mentioned in method section. This website updates data every day for all countries. As per the data available in this website on 25 th March 2020, coronavirus confirmed cases were reported from ~200 countries. Spearman statistical analysis found a negative correlation between MAET and coronavirus confirmed cases of a country (Correlation coefficient: -0.54 and p value: <0.0001). Further, this analysis found negative correlation between MAET and total active cases, (Table 1) . Our analysis also noticed a presence of a significant correlation between cases per million population and MAET (Correlation coefficient: -0.47 and p value: <0.0001) ( Table 1) . All these statistical results indicated that a negative link might exist between COVID-19 cases and environment temperature of a country. Just to verify all these results, we had further analyzed all the data by Kendall univariate method. Similar to Spearman method, Kendall analysis also found the negative association between environment temperature and various COVID-19 cases mentioned above. The observed values were highly dynamic, thus we have further analysed the data of every six days intervals (30 th March, 6 th April, 12 th April and 18 th April, 2020) to see the consistency of the statistical results among the data of different days. It was noticed that all these results from the analysis of these days' data found similar results to the data of 25 th March 2020 ( Table 1) . All these findings suggest that cold environment temperature might be sensitive to novel coronavirus (SAAR-CoV-2) infection.

To verify and support the study of earlier section, a log-linear model fitting approach has been adopted. The outcomes from modeling are shown in Table 2 and Figure 2 also. In Figure 2 , a matrix plot was drawn among temperature and three above mentioned cases for the observations collected at April 18, 2020. Here, the temperature is measured in °C while all other variables are represented on natural logarithmic scales, where the base is e. On the diagonal of matrix plot the histogram are plotted for temperature, cases per million, total cases and active cases respectively and helpful to identify the nature of the variables. In upper matrix plots, Pearson correlation coefficients (now, variables are in linearly relationship) was determined. It can be seen that a negative relationship exists among variables and temperature with correlation coefficient value -0.501, -0.455 and -0.426 for cases per million, total cases and active cases, respectively. The loglinear model was also estimated and fitted for covid-19 considered cases and temperature. In the lower matrix plots, all the fitted models are sketched. The estimated value for j=1, 2, 3 (for detail see Table 2 ). All estimators were also tested and the respective p-values are less than.0001. As the estimate of 1 β for all three cases are negative which establish that models show the decreasing behavior of variables with respect to temperature. In Figure 2 , to support the purposed log-linear model for fitting, we additionally present important characteristics using graphs such as residuals, Q-Q and Cook's distance plot. The Residuals and Q-Q plot showed that the data fulfill the assumptions of fitting a log-linear model. Here, it was observed that in Q-Q plot, few observations on both the tails behave abruptly. Cook's distance plot also shows similar behavior. These observations may also be treated as extreme values or outliers.

To establish the above findings, we used the observations particularly on other four days (25 th March, 30 th March, 6 th April and 12 th April, 2020). We have noticed the consistency for estimated results and all models following the same pattern as the data obtained from dated April 18, 2020 (Supplementary Figure S1-4) . If there is slightly differences in outcomes that can be understand because few countries had contributed from early days, while others had participated significantly in the study later on. This effect has been discussed in following section.

All these findings support our earlier establishment firmly that in warmer countries the chance of covid-19 patients is fewer than in colder countries. So the results can be drawn firmly from univariate analysis.

Negative relationship exists between environment temperature and COVID-19 cases of the countries having at least 50 cases or more:

It was noticed that all the countries under this study did not have the uniform COVID-19 observations till dated April 18, 2020. In fact, observation values of total cases (cases per million or active cases) are very low in countries lying in hotter temperature zone. So, observations from such countries may influence the performance of estimators significantly. So, a comparative analysis has been done for temperature and considered three variables as per the total number of cases (Table 3) . Here, we grouped the countries as they have greater than or equal to 1, 50, 100 and 1000 total number of COVID-19 cases at the date of study. In Table 3 , one row mentioned the number of countries comes under these groups at a particular day. We calculated Karl Pearson correlation coefficient for log-linear data with Spearman and Kendall correlation coefficients. Particularly, the mean, median and standard deviation (SD) of temperatures were measured for a group designed as per total number of cases.

It was noticed that the number of countries was consistently increasing in each group from first day of study to till recent day ( Table 3) . Other variables those have the influence of these designed groups in table were descriptive measures of temperature. For example, at dated 18 th April for cases per million, the mean of temperature in among groups varied from 19.8°C to 15.5°C and similarly for median of temperature from 22°C to 13.5°C with SD (standard deviation) from 8.0°C to 7.6°C. These measures are getting a wider range i.e. average minimum and average maximum difference as approaches toward back days. Similar behavior can be seen for total cases and active cases columns ( Table 3) . If the number of country in a group is lesser then respective temperature SD is also small. This is obvious because of as total cases restriction increases the countries in a group comes from a more homogenous temperature zone. So, as the restriction of total number of cases increases from 1 to 1000 the average temperature and SD decreases simultaneously. These homogenous temperature zones impact can be seen through the respective correlation coefficients directly. For example, at dated 18 th April, 2020 for active cases as average temperature reduces from 19.8°C to 15.5°C/22°C to 13.5°C for mean/median with number of countries from 200 to 78 and hence correlation coefficients increase drastically from -0.45 to -0.15, -0.42 to -0.12 and -0.29 to -0.07 for Pearson, Spearman and Kendall coefficient, respectively. The similar outcomes can be seen for other cases and as well as on other days. So, it can be interpreted as the correlation coefficient is decreasing because of it is calculated for within more homogenous time zone countries. That is why it is approaching toward the zero and trying to show an independent relationship with larger total number restriction. Here it is to be noted that as one is going backward in days for within group the correlation coefficients are performing consistently. So, the results drawn on the behalf of earlier table can be considered stable.

All these findings further suggest that a negative link exists between environment temperature and COVID-19 cases.

Recent ongoing pandemic of COVID-19 increases rapidly throughout the world, which emerged in Wuhan, China in the end of the year 2019. As of 18th April 2020, more than 2.4 million confirmed cases have been reported from 200 countries. This fatal outbreak has already killed almost 150000 individuals worldwide. In the last few weeks, the numbers of infection cases and deaths had been found to increase with a tremendous high rate. Till date, no medicines or vaccines are available for its treatment. All countries are strictly forcing various strategies such as travel restrictions, social distancing and quarantines as preventive measures. To prevent infection, the travel connection between different countries, states and cities has been restricted from the end of March, 2020. Various countries have also imposed the curfews and lock down to prevent the direct contact between individuals. In this context, many people, scientists, researchers and others think that novel corona virus infection may be decreased as the summer comes, i.e., as the environment temperature increases. It has been reported that infection and transmission of many virus types including influenza depends on air temperature and humidity 10, 11 . However, no such report has yet been published in case of this viral infection and/or transmission. The recent report suggests that the viability of this new coronavirus greatly varies on different object surfaces like stainless steel, plastic, cardboard, copper, etc., 12 . Here, object surface temperature might influence the longevity of the active virus particles. Thus, this study has given an attention to see the link between air temperature and COVID-19 cases. It was carefully observed that countries having larger COVID-19 cases are mostly located above of the latitude of Wuhan, which indicated that there might a negative relationship between temperature and COVID-19 cases (Figure 1) . Next, the country wise statistical analysis by various statistical methods have found a significant negative correlation between various COVID-19 cases including total cases, active cases and cases per million of a country with MAET. Similar negative relationships between these two parameters with MAET were found when the data of the countries having at least 50, 100, 1000 or more COVID-19 confirm cases ( Table 3) . All these observations suggest that low temperature might be a risk factor for COVID-19 cases. Next, we wanted to know whether the summer temperature can decrease this viral infection. Similar to our study, Chan KH et al 2011 reported that viability of other type of SARS coronavirus was decreased with high temperature and low humidity environment 13 . Their study also suggests that tropical countries having low risk of SARS coronavirus infection as compared to relatively cold countries. Thus, this current study gives a hint that the summer may prevent the SARS-CoV-2 infection/transmission rate as compared to the current season. Cold environment modulates many biological functions in our body. Our recent studies including other investigations have suggested that cold environment might be a risk factor for cancer diseases because cold adaption in long duration may provoke the cancer risk probably by altering various physiological and cellular functions with the influence of epigenetic changes, and bringing mutation in tumor suppressor genes 7, 8, [14] [15] [16] [17] . It was also reported that cold exposure decreased anti-tumor immunity to increase cancer growth and metastasis in animal model 18 . Similarly, various studies documented that cold environment is relatively susceptible to viral infection because of suppression of immune response 19 . Studies also reported the link between cold environment and asthma 20 . In regard to COVID-19, all people may not be infected upon exposure of this novel corona virus. Some of them may be infected. Some infected people develop severe acute respiratory distress symptom 21 . The recent clinical study reported that severe cases of this virus infected patients have lower monocytes, eosinophil, basophils and T cells 22 . This viral infection may not much vary with respect to age of the individual, but fatality is more in case of old individuals 21 . The immune system of the old people may not support enough to provide adaptive and boost immune response to fight this novel coronavirus infection and its severity. In this situation, people living in cold environment might be an additional risk for COVID-19 severity. However, at this time, this study is unable to explain of how some cold countries have relatively higher coronavirus infected cases. Is it just the cold or are there additional factors? Second, is it possible that the warm temp kills more viruses but also the ones that are able to grow are less virulent and cause less damage? Moreover, extended analysis with a large set of data is required to prove this negative link between environment temperature and COVID-19 cases. The accumulating evidences reveal that the severity is large in cases of the COVID-19 patients having preexisting health issues like diabetes, hypertension, obesity, cancer, heart and kidney diseases 23, 24 . All published reports state that the virus enters inside the cells by endocytosis process, where the viral spike proteins (S) interact with cell surface receptor ACE-2 protein present over epithelial cells of the respiratory tract, lungs and other tissues 25, 26 . It has also been suggested that this coronavirus enters in the circulation and infect various organ tissues (kidney, cardiac muscles, colon, adipocytes, etc.) having higher expression of ACE-2 on their cell surface 27 . Here, the host cell serine protease TMPRSS2 primes spike glycoprotein (S) to facilitate infection 26 . For examples, type II diabetes may have high risk of corona virus infection because of high expression of ACE-2 expression in pancreas 28 . Similarly, adipose tissue expressed more ACE-2 than lungs epithelial cells, thus obese people may have also high risk of this viral infection 29 . Study reported that the patients with high BMI (body mass index) had severe form of this virus infection 30 . It has also been suggested that some drugs used for the treatment of hypertension and diabetes may increase the expression of ACE-2 which invites the higher risk of virus infection 23, 24 . All these findings suggest that the high expression of ACE-2 and TMPRSS2 in host cells is a great risk factor for this novel coronavirus infection. Moreover, implemented strategies for preventive measures to control the spreading of infection greatly vary from one country to others, which may also affect this negative relationship. Moreover, sensitivity of coronavirus (SARS-CoV-2) to temperature may vary from one mutant type to others. In addition, the efficacy of such infection may differ in various ages, races, ethnicities and gender. Beside these, infectivity may also be dependent on various other local contextual factors like hygiene practices, population crowding, and living style 31 . This study has been done at very early stage as COVID-19 becomes a worldwide outbreak. And due to some limitations, the comorbidity factors are not included at this study. In future work, we can upgrade the same result with great extent by considering additional covariates (e.g., factors like diabetes, obesity, hypertension, cancers, and humidity of a country) This pilot study proposes that high temperature may delay this novel coronavirus infection. Of course, experimental study can say whether and how cold environment augments coronavirus infection and/transmission. 

Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding

Cross-species transmission of the newly identified coronavirus 2019-nCoV

The 2019-new coronavirus epidemic: Evidence for virus evolution

Understanding of COVID-19 based on current evidence

Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia

Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: retrospective case series

Colder environments are associated with a greater cancer incidence in the female population of the United States

Is cholesterol a mediator of cold-induced cancer?

A link between cold environment and cancer

Roles of humidity and temperature in shaping influenza seasonality

Transmission of a 2009 pandemic influenza virus shows a sensitivity to temperature and humidity similar to that of an H3N2 seasonal strain

The Effects of Temperature and Relative Humidity on the Viability of the SARS Coronavirus

A link between cold environment and cancer

Combination of 247 Genome-Wide Association Studies Reveals High Cancer Risk as a Result of Evolutionary Adaptation

Increased cancer incidence in "cold" countries: An (un) sympathetic connection?

Cold-hearted: A case for cold stress in cancer risk

Baseline tumor growth and immune control in laboratory mice are significantly influenced by subthermoneutral housing temperature

Immune responses to exercising in a cold environment

The association between ambient temperature and childhood asthma: a systematic review

COVID-19 infection: the perspectives on immune responses: Cell Death Differ

Dysregulation of immune response in patients with COVID-19 in Wuhan, China

Are patients with hypertension and diabetes mellitus at increased risk for COVID-19 infection?

Coronavirus Disease 2019 (COVID-19): Do Angiotensin-Converting Enzyme Inhibitors/Angiotensin Receptor Blockers Have a Biphasic Effect?

A pneumonia outbreak associated with a new coronavirus of probable bat origin

Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor

Pharmacological Therapeutics Targeting RNA-Dependent RNA

From Mechanistic Studies to Clinical Trials for COVID-19

Endocrine and metabolic link to coronavirus infection

Two Things about COVID-19 Might Need Attention. Preprints.org

Risk of COVID-19 for patients with obesity: Obes Rev

Wuhan novel coronavirus (COVID-19): why global control is challenging? Public Health