Natural hot Corona near Corona

Added: Trae Gullo - Date: 20.02.2022 07:48 - Views: 27827 - Clicks: 3904

Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. Visual inspection of world maps shows that coronavirus disease COVID is less prevalent in countries closer to the equator, where heat and humidity tend to be higher.

Scientists disagree how to interpret this observation because the relationship between COVID and climatic conditions may be confounded by many factors. A one-degree increase in absolute latitude is associated with a 4. According to our , countries are expected to see a decline in new COVID cases during summer and a resurgence during winter. However, our do not imply that the disease will vanish during summer or will not affect countries close to the equator. Indeed, many viral acute respiratory tract infections, such as influenza A and B, rhinovirus, respiratory syncytial virus, adenovirus, metapneumovirus, and coronavirus, are climate dependent and share such seasonal patterns 1.

Some viruses may have better stability in low-temperature, low-humidity, and low-UV radiation environments 2 , 3. In addition, people tend to gather more in indoor places in winter, which can facilitate the spread of diseases; and vitamin D levels in humans tend to decline in winter, which may weaken the immune response. On April 7, , the U. Using data from cities in China, one article published in May found no such association 6. Yet another study published in November found a ificant negative association between temperature and the spread of COVID using global data 8.

While, in general, the evidence is mixed and the debate is still ongoing, laboratory studies found that SARS-CoV-2 is highly susceptible to heat and UV-radiation 9 , 10 , 11 , 12 , 13 , To add evidence from a different perspective, we use global data to examine the relationship between climatic conditions and the spread of COVID controlling for several important confounding factors. To this end, we regress the prevalence of COVID logarithmically transformed at the country level against the latitude of a country.

Latitude captures every climate, because different latitudes on Earth receive different amounts of sunlight. Furthermore, latitude also affects humidity, because water evaporation is temperature dependent Figure 1 and Table 1 show our . In general, the farther a country is located from the equator, the more cases the country has relative to the of inhabitants. This relationship is visible in the scatterplot in Fig. In the ordinary least squares OLS regression, in which we control for all potential confounding factors, an increase in the distance from the equator by one degree of latitude is associated with an increase of the prevalence of COVID by about 4.

Our are consistent with the hypothesis that heat and sunlight reduce the spread of SARS-CoV-2 and the prevalence of COVID, which was also suggested by most of the studies examining the same hypothesis with different data and approaches 8 , 9 , 10 , 27 , However, our do not imply that the disease will vanish during summer.

Our analysis has several limitations. First, while our are consistent with the hypothesis that higher temperatures and more intense UV radiation reduce SARS-CoV-2 transmission, the precise mechanisms for such an effect remain unclear and may indeed comprise not only biological but also behavioral factors.

For example, people might gather less in crowded indoor places if temperatures are higher — a behavior reducing transmission. Thus, future research should aim at uncovering how the transmission of SARS-CoV-2 is affected by changes in 1 climatic factors such as heat and humidity, 2 geographic factors such as altitude and sunlight intensity, 3 factors related to human behavior such as social interactions and pollution due to local economic activity at a more disaggregated level, and 4 the different potential of the human immune system to cope with diseases in summer as opposed to winter.

Third, while we strived to control for differential testing intensity using a recently compiled and frequently updated data set 19 , 20 , the data on testing intensity could suffer from reporting biases and incomplete coverage of testing approaches.

The fact that column 4 in Table 1 contains a parameter estimate of latitude that is only slightly lower than the one in column 3 and still highly ificant is reassuring in this regard. Furthermore, factors such as health infrastructure, socioeconomic background, and the availability of adequate health supplies may also affect the spread of COVID However, these differences can be at least partially captured by controlling — as we have done — for vehicle concentration, urbanization, cell phone usage, income, the old-age dependency ratio, health expenditure, and testing intensity.

Fourth, we cannot, as of yet, assess whether mutated versions of SARS-CoV-2, such as the ones that emerged in South Africa or in the UK in fall , will display similar seasonal patterns of infection. Finally, the distance to the equator has the same climatic effects going north and south only when we are either around equinox or when one full year in the pandemic has passed such that the seasonal variations average out globally because both hemispheres have passed through all four seasons during the pandemic.

Thus, the date of our data set which we updated during the final revision of this manuscript in January is comparatively well-suited for our analysis, because at this point in time the COVID pandemic had been spreading for approximately 1 year 31 , Moreover, the effect sizes we estimate stayed rather stable over time.

In earlier analyses of the data in March and April 33 , which is close to equinox, we also found a ificant positive association between latitude and the of cases. Since then, the semi-elasticity estimates increased slightly, which could be due to better data quality and larger s of observations in our updated data sets. Increasing temperatures and longer sunlight exposure during summer may boost the impact of public health policies and actions to control the spread of SARS-CoV Conversely, the threat of epidemic resurgence may increase during winter.

However, our do not indicate that the disease will vanish in summer, nor that countries located close to the equator will contain the disease without effective public health measures. We estimated both the bivariate specification of the regression of the logarithm of COVID cases per million inhabitants on latitude as well as nested models with control variables. We excluded countries in which less than COVID cases were reported as of January 9, , to use only data from countries where the pandemic was spreading a few cases could be merely imported.

Our main exposure variable is the absolute latitude of a country in degrees. The control variables included: 1 air travel, measured by the of air passengers per capita in a country; 2 vehicle concentration, measured by the of registered vehicles per capita; 3 urbanization, measured by the percentage of the population living in cities; 4 testing intensity, measured by the of tests per hundred inhabitants; 5 cell phone usage, measured by the of cell phones per capita; 6 income, measured by purchasing power-adjusted per-capita gross domestic product in a country; 7 old-age dependency ratio, which is the ratio of the population above the age of 65 to the working-age population; 8 health expenditure, which is the share of per-capita GDP spent on health.

We used data for air travel, vehicle concentration, income, urbanization, cell phone usage, old-age dependency ratio, and health expenditure, because more recent data were not available in the World Development Indicators, our data source for these variables Testing intensity was based on testing data gathered for each country 19 , We used robust standard errors to for heteroscedasticity. We used Stata 16 for our multivariable regression analyses. We estimated missing country covariate data in multiple 15 imputations, using the mibeta Stata command Stewart, P.

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Buying time for an effective epidemic response: the impact of a public holiday for outbreak control on COVID epidemic spread. Engineering 6 , — COVID and climate: global evidence from countries. Rubin, D. Multiple Imputation for Nonresponse in Surveys Vol. Download references. The funders had no role in study de, data collection and analysis, decision to publish, or preparation of the manuscript. You can also search for this author in PubMed Google Scholar.

Natural hot Corona near Corona

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