key: cord-1001551-dxq7m826 authors: Ozyigit, Ahmet title: Understanding Covid-19 Transmission: The Effect of Temperature and Health Behavior on Transmission Rates date: 2020-07-08 journal: Infect Dis Health DOI: 10.1016/j.idh.2020.07.001 sha: 38fbbe1b3a16485657533f4c1723316f7bdbd694 doc_id: 1001551 cord_uid: dxq7m826 BACKGROUND: Covid-19 pandemic is an uncharted territory for the world’s population. Countries are seeing measures they would have never considered under democratic governance in an attempt to contain case numbers. The role of outside air temperatures have been implicated as a potential factor involved in disease transmission. However, to this date, there has been no clear evidence to suggest either way. Along with temperatures, infection control and protection measures as well as how well people adopt these measures are likely to play a role in disease transmission and case growth rates seen across countries. METHODS: The current study uses panel data estimation for the original EU-15 countries in an attempt to explain the role of outside air temperatures, health behavior and government-imposed containment measures on Covid-19 transmission rates. RESULTS: The preliminary evidence suggests that containment measures are highly effective in slowing down the spread of Covid-19. Years of education also appears to have a small but negative association with disease transmission rates suggesting that populations with higher educational attainments may be doing a better job of self-protection. Temperature appears to have a very small, but statistically significant impact on the viral transmission rate where a 1 degree Celsius increase in temperatures is estimated to reduce Covid-19 transmission by 0.9 percent. CONCLUSION: Results are robust and clear. Temperature plays a small but significant role on Covid-19 transmission rates. However, it is quite possible that we may not have yet reached temperatures which may exert more pronounced effects on viral activity. Further research is warranted when more data becomes available, especially covering the months of July and August. Coronaviruses periodically make headlines with deadly strains like the SARS-CoV, MERS-CoV and finally, SARS-CoV-2. Since the first identified case in December 2019 in the city of Wuhan in China, SARS-CoV-2 has spread globally, resulting in hundreds of thousands of deaths worldwide from a condition it causes known as CoVid-19. As of mid-March, the total number of cases in China had been surpassed by the number of cases in the rest of the world (Root, 2020) . The World Health Organization (WHO) has classified the disease a pandemic with over 210,000 active cases and more than 8,000 deaths worldwide as of March 11, 2020 (WHO, 2020 . With more countries reaching triple and quadruple digits of active cases, lockdowns and state of emergency declarations have been a common practice worldwide. While individual countries are taking their own measures in an attempt to slow down or contain the viral spread, the severity of measures are quite asymmetrical across the globe. China, as the origin of the pandemic, has successfully reduced the incidence of new cases through the employment of highest level of national response management protocols (WHO, 2020). Although less aggressive, Singapore, Taiwan and Hong Kong also appeared to have their case numbers in control in the early phases of the viral spread through early implementation of travel restrictions, social isolation and quarantine measures (Cowling & Lim, 2020) . However, those countries following more relaxed strategies initially only to experience exponential case explosions had to resort to lockdown and state of emergency measures to slow down the spread as in the cases of Italy, Spain, France and many more to follow afterwards (Elbaum, 2020) . With more cases being reported and the death toll rising, governments are trying to find a balance between prioritizing measures to reduce mortality and keeping an acceptable level of economic activity. In the meanwhile, experts are continuously trying to observe virus characteristics and behavior that might shed light on its transmission, spread and possible protective measures. The relationship between viral infections and meteorological conditions has been of interest in the past as an attempt to better understand virus behavior. It has been proposed that warmer climates can possibly slow down the spread of viral infections. The SARS-COV strain of the virus was observed to survive longer on surfaces at lower temperatures and low relative humidities (Casanova, Jeon, Rutala, Weber & Sobsey, 2010). The recent MERS-CoV endemic in the Middle East was observed to have a negative relationship between temperature and case incidences in a case-crossover analysis. Authors also reported a positive association with humidity (Gardner et al., 2019) . Another study focusing on the SARS-CoV strain found a statistically significant difference between viral inactivation using four, twenty and forty-degree In this study, the rate of spread of SARS-CoV-2 is being considered as a function of outside air temperature as an environmental factor and years of schooling as a behavioral component. We test the hypothesis that warmer climates and increased years of schooling are possible factors that can slow down the spread of the SARS-CoV-2 pandemic. The rest of the paper is organized as follows: Section 2 introduces the data and the model used in this study while results are discussed in section 3. This is followed by a brief conclusion in section 4. This study focuses on the Covid-19 transmission rates in the original EU-15 countries. EU-15 countries share many similar features in terms of population structure, social and health indicators as well as economic coherence. These similarities help minimize the impact of major confounders in cause and effect relationships we are interested in studying. A total of 60 days is covered since each country's 100 th reported case. The 100 th case is often used in literature as a benchmark when referring to Covid-19 experience. This is possibly a standardization attempt due to heterogeneity of testing practices and volumes at the initial stages. However, once each country has had 100 confirmed cases, it is safe to assume that more widespread testing and contact tracing would be in order. Daily high temperatures are used in correspondence with the daily case numbers for the 60-day period for each country. PrevRate represents the rate of growth of the daily case numbers for the fifteen countries used in this analysis. A logarithmic transformation is applied to the case numbers in an attempt to reduce data variability. Moreover, given that different countries employ different strategies, number of tests administered to the general population can significantly vary across the countries. Therefore, rather than absolute numbers, the rate of change in numbers will be of more assistance for the purpose of our analysis. Hence, we use the first difference operator. Temp variable is used for the daily average temperatures in the cities where disease transmission is most pronounced. Cities and associated summary statistics are given in table 1 below. Edu represents logarithmic transformation of the mean years of schooling for each country. 2018 statistics are used for the mean years of schooling for each country. Cont is a dummy variable used for containment measures used by the relevant governments. A value of 1 is assigned for the days strict measures are introduced by the governments and a value of 0 is assigned for the days when these measures are not in effect. Table 1 provides an overview of the summary statistics for each city/country used in this study: Spain, Italy, United Kingdom, Germany and France have, by far, the fastest case growth experience in the EU-15 countries. What these countries have in common is, they all have a government response times over 10 days. However, a closer analysis is required to measure the precise effect of each variable on case growth rates in order to draw more reliable conclusions. Panel data approach allows the use of multi-dimensional data with cross-sectional and timeseries aspects in an attempt to unravel relationships across time and space. Panel data models use a number of estimation approaches. OLS estimation with pooled panel tends to be the most simplistic approach. However, pooled panel OLS does not recognize unique attributes between the cross-sectional entities (different countries used in this analysis). In other words, cross-sectional entities are assumed to be homogeneous. When we consider biological, environmental, and cultural differences, to name a few, across world populations, accepting homogeneity across world populations does not sound like a reasonable argument. As a result, in this paper, we consider random and fixed effects models which take into consideration crosscountry heterogeneities as well as cross-country and time-series heterogeneities, respectively. Wu Haussmann test helps identify the right model when estimating panel data output. Based on our test results, the null hypothesis of random effects cannot be rejected, therefore, the variable effects model appears to be more suitable for the current paper (Greene 2008 ). Table 2 below provides the estimation output for the three equations specified in this paper, using pooled OLS estimates as well as the estimates for the variable effects model. Note: *, ** and *** denote significance at 10%, 5% and 1%, respectively. In the constants row, the first number represents the coefficient for the variable Numbers in parentheses show the t-statistics for each coefficient Numbers in angle brackets depict the probability statistics R 2 is used for the goodness of fit of the model estimation. Covid-19 pandemic is an uncharted territory for the world's population. Countries are seeing measures they would have never considered under democratic governance. The current study uses data from countries with sufficient length of Covid-19 incidence data in an attempt to explain the role of outside air temperatures on viral transmission rates to foresee whether summer months may experience lower incidence rates. The impact of human factors such as health behavior is explored using education as a proxy. Moreover, government-imposed containment measures are assessed with respect to their effects on the rate of disease transmission. The preliminary evidence suggests that containment measures are highly effective in slowing down the spread of Covid-19. Years of education also appears to have a small but negative association with disease transmission rates. Therefore, one could argue that populations with higher educational attainments may be doing a better job of self-protection. Temperature appears to have a very small impact on the viral transmission rate. However, it is a statistically significant impact. It is quite possible that we may not have yet experienced a threshold temperature which may have more pronounced effects on viral activity. While it is unfortunate, case numbers continue to increase, and it is quite likely that more countries will soon have sufficient data for a larger panel data study. Therefore, further research is required in the upcoming days or weeks to study data with longer time length. It will be important to attempt a similar study by observing wider temperature differentials with a higher number of countries from different geographical regions. Coronavirus Cases Outside of China Exceed Chinese Total for First Time Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19) Opinion | They've contained the coronavirus. Here's How. Retrieved Coronavirus: Silence shrouds streets in cities across Italy Effects of Air Temperature and Relative Humidity on A case-crossover analysis of the impact of weather on primary cases of Middle East respiratory syndrome Environmental factors on the SARS epidemic: air temperature, passage of time and multiplicative effect of hospital infection Role of temperature and humidity in the modulation of the doubling time of COVID-19 cases Potential Factors Influencing Repeated SARS Outbreaks in China Education Improves Public Health and Promotes Health Equity COVID-19 situation update for the EU/EEA and the UK, as of 1 Econometric Analysis Highlights • Temperature appears to have a small but significant negative influence on Covid-19 transmission based on panel data evidence from the EU-15 countries According to panel data estimates, 1 degree Celsius increase in temperatures can result in a 0.9 percent decrease in Covid-19 transmission • Education, used as a proxy of health behavior, has a strong negative association with Covid-19 transmission rates based on panel data evidence • Containment measures, while different in each country, have a highly significant effect in reducing viral transmission among the population I would like to express my gratitude to the reviewers who provided excellent feedback and helped massively in the shaping of the final version of this article. The feedback was thorough and to the point. No human or animal samples have been used in this study; therefore, ethics approval has not been necessitated. This is to certify that the attached paper titled "Understanding Covid-19 Transmission: The Effect of Temperature and Health Behavior on Transmission Rates." is the original work of the author listed on this manuscript and is not being considered for publication elsewhere. All the sources have been referenced and authors given credit to. No conflict of interest to declare. No funding has been used during the conduction of this study.