Why does the COVID-19 outbreak in Mexico seem to be slower? Some hypotheses for testing

Why does the COVID-19 outbreak in Mexico seem to be slower? 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Some hypotheses for testing Manuel Elías-Gutiérrez, Manuel Mendoza-Carranza, Umon Takahashi-Aguilar, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-51450/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aim: The current paper aims to present and discuss some hypothesis about why in Mexico and in particular the Mexico City the epidemy of COVID-19 seems to be slower than other countries. Subject and methods: We compared daily UVI maximum and the reported COVID cases in Mexico City, New York, and Los Angeles and performed an ADONIS permutation test to look for significant differences between case numbers and its relationship with the UV index. We performed a quantile regression to explain the relation between UVI and daily COVID cases. We also investigated and analyzed other possible factors contributing to this slowdown because UVI cannot be the only explanation. Results: Although Mexican authorities have applied one of the smaller numbers of tests in the world, it is possible to identify a slowdown in Mexico's rate of infections. We found a negative and significant difference between the UV and registered cases among the three cities analyzed. However, this slowdown cannot be explained by a single factor. It is possible to hypothesize about the confluence of other factors as Vitamin D levels in Mexican population, lack of central air conditioning and the ventilation in houses and metropolitan transportation systems. Also, the Bacillus Calmette–Guérin vaccination could be part of the explanation of this phenomenon. Conclusions: Single parameter models as solar irradiation can explain partially the complicated spread of COVID-19 in different parts of the world. However, it is necessary to extend our scientific understanding of environmental effects, city infrastructure, population age, vaccination schemes, socioeconomic conditions, social and political behavior on COVID-19 epidemic dynamics. Epidemiology Ultraviolet Index data deficiency vaccination public transportation. Figures Figure 1 Figure 2 Introduction The rate of infection of COVID-19, is accelerating in most countries, particularly in the United States. In contrast, the infection rate appears to be slower in Mexico, despite populated areas of the country being ripe for wide-spread infection. For example, the Mexico City Metropolitan Area hosts, together with the suburbs in the State of Mexico, more than 20 million people (CONAPO 2010) in about 7954 km 2 , making human-to-human transmission highly favorable. The populace itself should also be highly susceptible to complications from COVID-19 due to limited sanitary services, poor air quality due to pollution, rampant diabetes, and obesity. Additionally, while the federal government initially explained that they were sampling accordingly with the Sentinel Surveillance model, developed by the World Health Organization (WHO) for other pandemics, it seems that strategy was abandoned following an intense polemic. Mexico is one of the principal economies in Latin America and the tenth most populous in the world with 129 million people. However, COVID-19 studies are scarce and most of them are descriptions about the general situation and development of the pandemic (e.g. Centeno 2020; Ñamendys-Silva 2020; Ramos 2020). Consequently, possible causes of the growth epidemic rate of Mexico cases are unknown, being the lowest in the world for testing (Table 1). Taken together, one would expect this country to have a high number of COVID-19 cases, and the outbreak should certainly not be slowing for no reason this early in the pandemic. In this article, we sought to understand the reasons for this phenomenon better. Our goal was to inquiry why the reported number of infections in Mexico is lower than expected based on population size and the aforementioned factors. We focused our discussion in a series of potential environmental factors that could be related with the lower rate of infections observed especially in Mexico City, as the solar UV irradiation (Sagripanti and Lyte 2007; Gunthe et al. 2020), the ventilation of houses and public transport, and Mexican vaccination scheme. It is important to highlight a possible gap in epidemic statistics based in the low number of COVID-19 tests in relation to population size in Mexico (Theodoros et al. 2020). Materials And Methods Checking if there is a slowdown We determined the number of patients in hospitals in Mexico City, using COVID-19 tracking apps developed by the government (Gobierno de la Ciudad de México 2020a) and examined several institutions as the National Institute of Respiratory Diseases. Also, we examined the number of casualties in the whole country. Comparisons We focused on Mexico City and compared it with two cities in the United States (New York and Los Angeles). We compared the whole country with other Latin American countries. We concede that no country can know their correct number of cases, as that would require continuous testing of every individual in their population. However, the number of cases in Mexico is assumed to be much higher than reported because of Mexico’s below-average testing. UV Irradiance We inquiry, among other factors, that there might be a causal relationship between local UV index (UVI) and the number of COVID cases. To test this hypothesis, we used as model the maximum daily UVI and reported COVID positive cases in Mexico City (CDMX), New York (NY), and Los Angeles (LA) from March 12 th to June 26 th , 2020. Data Acquisition All data used here are public. UV index (UVI) values were obtained for New York (NY) and Los Angeles (LA) from the UV-B Monitoring and Research Program of the Colorado State University (Colorado State University 2020). For Mexico City (CDMX), UVI values were obtained from two sources: 1) the solar radiation records of the CDMX Government (Gobierno de la Ciudad de México 2020b) for March data; and 2) the Weather online website (WeatherOnline Ltd 2020) for April and May data. Confirmed COVID cases for NY and LA data were obtained from the NYC Health Department (2020) and Department of Public Health (2020) respectively. Mexico COVID data were obtained from the COVID-19 information site from Mexican Government (Gobierno de México 2020). Statistical Analysis Daily case numbers were standardized to cases per ten thousand inhabitants (=10ti). An ADONIS permutation (1000) test was performed to look for significant differences between the number of COVID cases and its relationship with the UV index. Finally, a quantile regression analysis was performed to explain the relation between maximum daily UVI and daily COVID cases per ten thousand inhabitants (Koenker and Hallock 2001; Koenker 2005), good of fit was measured using the pseudo R 1 calculation (Koenker and Machado 1999). Source of Information for Hypotheses Proposed Finally, we consulted the Web of Science (https://webofknowledge.com) to gather all COVID-19 updates until June 15 th , 2020, mainly related to UV, BCG vaccination, and other themes that could help to explain the slowdown of the pandemics in Mexico. Results Slowdown of Infections in Mexico From 78 hospitals designated as COVID-19 in CDMX (a low figure for the size of the city), 18 were still with places, according to the official site of the Mexican Government (Gobierno de la Ciudad de México 2020a). The average daily COVID cases for CDMX were 0.56 ± 0.41 cases per 10ti, while LA had 2.42 ± 1.33 cases per 10 ti, and NY had 2.35 ± 2.11 cases per 10 ti (Fig. 1A). Total number of deaths in Mexico by June 23 th was 22,580, a higher number in proportion to detected cases than most countries (Roser et al. 2020). UV index and COVID relation Average UVI followed the opposite trend, with CDMX having the highest score (12.01 ± 0.47), followed by LA (8.26 ± 1.23) and NY (6.37 ± 2.6) (Fig. 1B). The Adonis permutation test indicates significant differences among positive COVID cases numbers between the three cities (p= 0.0009) and UVI scores (p=0.0009). In addition, we found an independent effect of the UVI on the number of COVID cases between each city (p=0.0009, Table 2) and the effect of City and UV (P=0.0009). Quantile regression analysis shows a negative relation between COVID case number and the increase of UVI. The pseudo R 1 value is increased in relation with quantile increase reaching a maximum R 1 of 0.36 for 90 and 95 th quantiles (Table 3, Fig. 2). Discussion Gunthe et al. (2020) suggest a possible negative relationship between UV radiation, temperature, and COVID case numbers. However, their data are not conclusive because the adjusted curve diminishes on both sides around a peak, making it difficult to interpret. In China, Yao et al. (2020) found no relationship between average UVI and COVID case numbers, but in one region (Hubei), these authors show a possible negative correlation. Other unpublished studies have tried to resolve weather effects on a pandemic, with no conclusive results (Xu et al. 2020). Some possible bias in mentioned studies could be the use of daily UV averages. We used the maximum UVI per day (that goes from zero to maximum), producing a comprehensive relation between UVI and positive COVID-19 cases. Despite the observed relation, it is important to remark that the UVI is only one part of the explanation to the low prevalence of COVID cases in CDMX compared to similar cities as NY and LA. Paradoxically, CDMX has ideal conditions for a rapid spread of any pandemic due to population density and other factors as high prevalence of chronic diseases and hotspots of people accumulation, as the subway system (Metro). In addition to the UV effect, it is necessary to consider other factors that can explain the behavior of the COVID epidemic in CDMX and most of the country. One factor related to high UVI, additional to latitude, is the altitude above sea level. In the case of the Mexican Plateau, where CDMX and other major cities such as Toluca, Puebla, Querétaro, and Morelia are found, the average elevation is ≥2000 masl. For example, the UVB levels in a station located about 60 km southeast of CDMX (Tlamacas, at 4000 masl) had a maximum average irradiance of 400 mW/m2 of UVB in March, with a minimum near 250 mW/m2 in January. Two other localities at the same latitude, but at sea level, have around 300 mW/m2 (Manzanillo and Veracruz), with a minimum of about 150 mW/m2 on the same dates (Valdes-Barron et al. 2013). In addition to the amount of UV reaching the surface, the type of it also affects its capacity to inactivate pathogens. UV is found in the environment in three forms: UVA, UVB, and UVC. Each of these will reach the surface in different quantities after atmospheric filtration, so the type of UV reaching the surface (and subsequent germicidal effect) is also affected by altitude over sea level (Dwivedi et al. 2018). The effect of natural and artificial UV on virus inactivation has been extensively studied. For example, the influenza virus is inactivated by up to 99% if exposed to full sunlight during a single day in Mexico City, but this value lowers to 66% in the winter (Sagripanti and Lyte 2007; Sagripanti and Lytle 2020). Another study demonstrated that aerosolized MHV Coronavirus could be highly sensitive to 254 nm UV-C, but it is dependent on the culture medium (Walker and Ko 2007). The effect of the UV on viral inactivation is thought to be through uracil dimerization in the RNA (Woo et al. 2012). These authors also found that UVC inactivates a bacteriophage virus more efficiently in aerosols (with differences depending on the media) than droplets. Other Factors Proposed to Explain the Slowdown Differences in the behavior of COVID-19 development in Mexico (and possibly other countries at a similar latitude) compared to more northern countries can only be partially explained by the UV effect and altitude. We suggest some other factors that need to be included in a more holistic model to explain the slowdown COVID-19 infection in Mexico and CDMX. A possible link between solar radiation-Vitamin-D production and the increase of immunological defenses against viruses (Grant 2008). Although controversial, the effect of vitamin D deserves further study because it seems to have a positive result against this type of infection (Kakodkar et al. 2020). Laird et al. (2020) suggest a positive relation between immune system and vitamin D regulating cytokine response to pathogens, fact confirmed by Daneshkhah et al. (2020) Another factor influencing the COVID-19 epidemic behavior in Mexico could be the vaccination scheme. Recently, researchers have suggested that Bacillus Calmette-Guérin (BCG) vaccination against tuberculosis could diminish the spread of COVID-19 (Gupta 2020). This factor has three possible explanations: 1. The molecular similarity between BCG antigens and viral antigens. After vaccination, memory B and T cells recognize both BCG and respiratory pathogens; 2. BCG could lead to antigen-independent activation of bystander B and T cells; and 3. Long-term activation and reprogramming of innate immune cells called trained immunity. Monocytes undergo epigenetic histone modifications at promoter sites of genes encoding inflammatory cytokines, leading to long-term changes in their ability to respond to novel stimuli (Redelman-Sidi 2020). This effect leads the activation of Th1 immunity and downregulation of Th2, and the consequent reduction of local tissue destruction by SARS-Cov-2 (Gupta 2020). Indeed, there appears to be a significantly lower prevalence of COVID-19 in countries where it is mandatory for BCG vaccination. In Mexico, massive vaccination with BCG started in 1951 and continues up to the present (Hurtado-Ochoterena and Matías-Juan 2005). It is also essential to notice that most houses in Mexico City have no central air conditioning or insulation system, unlike the northern temperate regions with severe weather. As a result, Mexican houses are sunny and well ventilated. Th air currents inside houses do not allow small particles to remain inside rooms or houses for a long time, diminishing the probability of infection. This hypothesis was analyzed, albeit only in a small number of studies (Hobday and Dancer 2013). Despite the effect of sunlight inside houses is not currently known it is possible that play a similar role as in the environment, neutralizing virus particles by UV radiation. Additionally, a sunny house represents a key element in preventing depression (An et al. 2016; Fleming et al. 2018) reinforcing the immune system (Szałach et al. 2019; Cruz-Pereira et al. 2020). Similarly, the primary modes of metropolitan public transportation in Mexico also lack air conditioning, including the vast subway system (Metro) of CDMX used by hundreds of thousand people daily. These transportation systems always operate with the windows open and air circulation inside tunnels is enhanced using ventilation system. Overall, the citizens of Mexico live under conditions less favorable to virus survival on outdoor/public surfaces. As we present along this paper, there are evidences that mentioned factors (UVI, vaccination scheme, airflow) could reduce the spread velocity of COVID-19 infection. However, no current models or data exist to explain how these factors affect the spread velocity of COVID-19 infection in large cities as CDMX. This city has a high density of population, with many people having pre-existing chronic diseases as diabetes, high blood pressure and obesity making them more susceptible to viral diseases (Gnatiuc et al. 2019; Rodríguez-Torres and Casas-Patiño 2019) We do not know precisely how each factor mentioned here contributes to lessening the spread of COVID-19. Nevertheless, the possible synergy of all of them appears to slower this pandemic. Currently, no model exists that can take all these factors into account. We, therefore, consider the aforementioned ideas as requiring rigorous testing. Remarks on Data Gathered A common weakness of any model is the lack of data. In the case of COVID-19, we can be confident that this is the principal limitation in explaining the epidemic development in Mexico and elsewhere (Burdorf et al. 2020; Naudé 2020). If we add the asymptomatic people not reported but still contagious (Bai et al. 2020), the uncertainty may be higher. It is known that people’s behavior is related to COVID-19 spread rates (Van Bavel et al. 2020). However, in certain conditions it is tough to calculate this relation, which is another source of uncertainly in the prediction models. It has been suggested that the number of fatalities could be three times higher than it currently is in Mexico (Ahmed 2020). This figure would show 10-12,000 fatalities for the country and is currently not supported by hard data by May 8 th . Except for Brazil, most Latin American countries are experiencing a slow spread of COVID-19. However, these data can be misleading due to a lack of testing, which is low in every Latin American country (Table 1). Despite a lack of testing, the pandemic's true extent will eventually become evident if COVID-19 cases overwhelm hospitals and funerary services, as they have in Ecuador (mainly in the city of Guayaquil). We presume this slowdown will slow the rate of infection but also make it continuous throughout the summer complicating the function of sanitary services, including hospitals. Mexican authorities are working to solve the lack of services and resources needed to confront a massive infection of COVID-19, but already several hospitals have become a point of infection due to the lack of materials and poor installations (Kitroeff and Villegas 2020). Conclusions Single parameter models can explain partially the complicated spread patterns of COVID-19 in different parts of the world. We expect that the hypotheses presented here and other efforts will help to establish the causes of the differential epidemic spread observed. Other authors have marginally discussed part of the ideas exposed here (Zhang et al. 2020), although their work is controversial. Finally, we need to extend our scientific understanding of environmental effects, city infrastructure, population age, vaccination schemes, socioeconomic conditions, social and political behavior on COVID-19 epidemic dynamics. Otherwise, the decisions taken by the governments and people will be uninformed and potentially erroneous. Declarations ACKNOWLEDGEMENTS Sean Prosser (University of Guelph) critically reviewed the manuscript and assisted us with the English version. Competing Interests The authors declare they have no competing interests References Ahmed A. (2020) Hidden Toll: Mexico Ignores Wave of Coronavirus Deaths in Capital. New York Times. https://www.nytimes.com/2020/05/08/world/americas/mexico-coronavirus-count.html Accessed 9 May 2020 An M, Colarelli S M, O’Brien K, Boyajian M E (2016) Why we need more nature at work: Effects of natural elements and sunlight on employee mental health and work attitudes. 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Yao Y, Pan J H, Liu Z X, Meng X, Wang W D, Kan H D, Wang W B (2020) No association of COVID-19 transmission with temperature or UV radiation in Chinese cities Eur Respir J. 55: 2000517. https://doi.org/10.1183/13993003.00517-2020. Zhang R, Li Y, Zhang A L, Wang Y, Molina M J (2020) Identifying airborne transmission as the dominant route for the spread of COVID-19. Proc. Natl. Acad. Sci 117:14857-14863. https://doi.org/https://www.pnas.org/cgi/doi/10.1073/pnas.2009637117. Tables TABLE 1 Total COVID-19 tests per thousand people by June 8 th or 9 th in some countries of the Americas. *People tested; **Units unclear ***Samples tested + Tests performed. Country Total Tests/1,000 persons Date of the last report (2020) Canada 75.43 Jul 2 United States 101.09 Jul 2 Peru 7.76 Jul 3 Colombia 15.69 Jul 2 Ecuador 6.82 Jul 2 Mexico 4.04 Jun 26 Bolivia 6.63 Jul 1 TABLE 2 ADONIS permutation test results for COVID cases number in New York, Los Angeles and Mexico Cities. Main effect d.f. SS MS F.model R2 Pr(>F) City 2 11.61 5.80 58.19 0.221 0.0009 UV index 1 3.15 3.15 31.62 0.060 0.0009 City*UV index 2 7.59 3.79 38.03 0.144 0.0009 Residuals 183 30.13 0.09 0.574 Error 188 52.48 1.0 TABLE 3 Quantile regression estimates of B0 and B1, 95% confidence intervals for B1, pseudo R 1 and P for Ho: B1 = 0 for four regression quantiles between UVI and COVID positive cases per 10 thousand inhabitants in New York, Los Angeles and Mexico City. T (quantile) Bo B 1 95% CI for B 1 R 1 p 20 th 1.82 –0.13 –0.16 – 0.12 0.19 P < 0.001 50 th 4.13 –0.30 –0.35 –0.23 0.23 P < 0.001 90 th 9.33 -0.66 –0.83 –0.53 0.36 P < 0.001 95 th 11.18 –0.79 –0.87 –0.34 0.36 P < 0.001 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-51450","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":1116363,"identity":"b99e88d4-e02d-4c95-93cf-7dfe715f6d0d","order_by":0,"name":"Manuel Elías-Gutiérrez","email":"","orcid":"https://orcid.org/0000-0003-2562-4584","institution":"El Colegio de la Frontera Sur","correspondingAuthor":false,"prefix":"","firstName":"Manuel","middleName":"","lastName":"Elías-Gutiérrez","suffix":""},{"id":1116364,"identity":"17678995-10b4-4d3f-a0e4-553506fff99b","order_by":1,"name":"Manuel Mendoza-Carranza","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYFAD9gaStfAcIFmLRAKRCnXbDz/7XFFzx55/5uODHz78OZzPIHbGAK8WszNpxjPPHHvGLHE7LVlyZtthywbpNPz2md1gMGZsYDvMxnA7x4yZt+GwAYN08gECWtg/Mzb8O8wjf/P8N2aePyAtiQ0EtPAYMza2HZYwuMHDxszDRowtZ3KKGRv7DhsYAj0F9Eu6ARtBvxw/vpmx4dthe7njhx8CQ8zagF86B3+IYQI2EtWPglEwCkbBKMACAKR8Q7/TrH6OAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-8216-2115","institution":"El Colegio de la Frontera Sur","correspondingAuthor":true,"prefix":"","firstName":"Manuel","middleName":"","lastName":"Mendoza-Carranza","suffix":""},{"id":1116365,"identity":"47d4b98a-b9a9-439a-83e8-7b270a23bcf2","order_by":2,"name":"Umon Takahashi-Aguilar","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Umon","middleName":"","lastName":"Takahashi-Aguilar","suffix":""},{"id":1116366,"identity":"ac81b00f-06fc-41f7-801d-5f8e75281a31","order_by":3,"name":"Martha Valdez-Moreno","email":"","orcid":"","institution":"El Colegio de la Frontera Sur","correspondingAuthor":false,"prefix":"","firstName":"Martha","middleName":"","lastName":"Valdez-Moreno","suffix":""},{"id":1116367,"identity":"938bf972-2bf5-45fd-a458-c018092cb967","order_by":4,"name":"Jenny Alexandra Prado-Bernal","email":"","orcid":"","institution":"Clínica Carranza","correspondingAuthor":false,"prefix":"","firstName":"Jenny","middleName":"Alexandra","lastName":"Prado-Bernal","suffix":""},{"id":1116368,"identity":"2cfa7bac-4ddc-4f3a-88c2-34f7557c8647","order_by":5,"name":"Claudia Mora-Loya","email":"","orcid":"","institution":"Clínica Carranza","correspondingAuthor":false,"prefix":"","firstName":"Claudia","middleName":"","lastName":"Mora-Loya","suffix":""}],"badges":[],"createdAt":"2020-07-30 21:55:09","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-51450/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-51450/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":1771183,"identity":"a5fe1dde-e30e-472b-b7ba-457848530884","added_by":"auto","created_at":"2020-08-03 22:10:49","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":346008,"visible":true,"origin":"","legend":"Daily confirmed cases/10,000 inhabitants in NY (magenta), LA (blue), and CDMX (green) from March 9th to June 26th and maximum daily in the same dates for UV index. Number of cases of cities are located in axis x at the average UVI index for each.","description":"","filename":"coviduvJUN.jpg","url":"https://assets-eu.researchsquare.com/files/rs-51450/v1/coviduvJUN.jpg"},{"id":1771184,"identity":"a1f805ec-c57f-482f-881a-5b3b40fd4240","added_by":"auto","created_at":"2020-08-03 22:10:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":714242,"visible":true,"origin":"","legend":" UVI index versus positive COVID cases by 10 thousand inhabitants. Red lines are the 20th, 50th, 90th and 95th quantile regressions","description":"","filename":"quantileregressionCOVIDJUN.jpg","url":"https://assets-eu.researchsquare.com/files/rs-51450/v1/quantileregressionCOVIDJUN.jpg"},{"id":13567181,"identity":"54cd7831-b98f-4dc2-8830-473d87ffd997","added_by":"auto","created_at":"2021-09-17 03:31:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":360067,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-51450/v1/54edfa4c-2095-4975-b313-ad2c9b8b549b.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eWhy does the COVID-19 outbreak in Mexico seem to be slower? Some hypotheses for testing\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe rate of infection of COVID-19, is accelerating in most countries, particularly in the United States. In contrast, the infection rate appears to be slower in Mexico, despite populated areas of the country being ripe for wide-spread infection. For example, the Mexico City Metropolitan Area hosts, together with the suburbs in the State of Mexico, more than 20 million people (CONAPO 2010) in about 7954 km\u003csup\u003e2\u003c/sup\u003e, making human-to-human transmission highly favorable. The populace itself should also be highly susceptible to complications from COVID-19 due to limited sanitary services, poor air quality due to pollution, rampant diabetes, and obesity. Additionally, while the federal government initially explained that they were sampling accordingly with the Sentinel Surveillance model, developed by the World Health Organization (WHO) for other pandemics, it seems that strategy was abandoned following an intense polemic.\u003c/p\u003e\n\u003cp\u003eMexico is one of the principal economies in Latin America and the tenth most populous in the world with 129 million people. However, COVID-19 studies are scarce and most of them are descriptions about the general situation and development of the pandemic (e.g. Centeno 2020; \u0026Ntilde;amendys-Silva 2020; Ramos 2020). Consequently, possible causes of the growth epidemic rate of Mexico cases are unknown, being the lowest in the world for testing (Table 1). Taken together, one would expect this country to have a high number of COVID-19 cases, and the outbreak should certainly not be slowing for no reason this early in the pandemic. In this article, we sought to understand the reasons for this phenomenon better. Our goal was to inquiry why the reported number of infections in Mexico is lower than expected based on population size and the aforementioned factors. We focused our discussion in a series of potential environmental factors that could be related with the lower rate of infections observed especially in Mexico City, as the solar UV irradiation (Sagripanti and Lyte 2007; Gunthe et al. 2020), the ventilation of houses and public transport, and Mexican vaccination scheme. It is important to highlight a possible gap in epidemic statistics based in the low number of COVID-19 tests in relation to population size in Mexico (Theodoros et al. 2020).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003eChecking if there is a slowdown\u003c/p\u003e\n\u003cp\u003eWe determined the number of patients in hospitals in Mexico City, using COVID-19 tracking apps developed by the government (Gobierno de la Ciudad de M\u0026eacute;xico 2020a) and examined several institutions as the National Institute of Respiratory Diseases. Also, we examined the number of casualties in the whole country.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eComparisons\u003c/p\u003e\n\u003cp\u003eWe focused on Mexico City and compared it with two cities in the United States (New York and Los Angeles). We compared the whole country with other Latin American countries. We concede that no country can know their correct number of cases, as that would require continuous testing of every individual in their population. However, the number of cases in Mexico is assumed to be much higher than reported because of Mexico\u0026rsquo;s below-average testing.\u003c/p\u003e\n\u003cp\u003eUV Irradiance\u003c/p\u003e\n\u003cp\u003eWe inquiry, among other factors, that there might be a causal relationship between local UV index (UVI) and the number of COVID cases. To test this hypothesis, we used as model the maximum daily UVI and reported COVID positive cases in Mexico City (CDMX), New York (NY), and Los Angeles (LA) from March 12\u003csup\u003eth\u003c/sup\u003e to June 26\u003csup\u003eth\u003c/sup\u003e, 2020.\u003c/p\u003e\n\u003cp\u003eData Acquisition\u003c/p\u003e\n\u003cp\u003eAll data used here are public. UV index (UVI) values were obtained for New York (NY) and Los Angeles (LA) from the UV-B Monitoring and Research Program of the Colorado State University (Colorado State University 2020). For Mexico City (CDMX), UVI values were obtained from two sources: 1) the solar radiation records of the CDMX Government (Gobierno de la Ciudad de M\u0026eacute;xico 2020b) for March data; and 2) the Weather online website (WeatherOnline Ltd 2020) for April and May data. Confirmed COVID cases for NY and LA data were obtained from the NYC Health Department (2020) and Department of Public Health (2020) respectively. Mexico COVID data were obtained from the COVID-19 information site from Mexican Government (Gobierno de M\u0026eacute;xico 2020).\u003c/p\u003e\n\u003cp\u003eStatistical Analysis\u003c/p\u003e\n\u003cp\u003eDaily case numbers were standardized to cases per ten thousand inhabitants (=10ti). An ADONIS permutation (1000) test was performed to look for significant differences between the number of COVID cases and its relationship with the UV index. Finally, a quantile regression analysis was performed to explain the relation between maximum daily UVI and daily COVID cases per ten thousand inhabitants (Koenker and Hallock 2001; Koenker 2005), good of fit was measured using the pseudo R\u003csup\u003e1\u003c/sup\u003e calculation (Koenker and Machado 1999).\u003c/p\u003e\n\u003cp\u003eSource of Information for Hypotheses Proposed\u003c/p\u003e\n\u003cp\u003eFinally, we consulted the Web of Science (https://webofknowledge.com) to gather all COVID-19 updates until June 15\u003csup\u003eth\u003c/sup\u003e, 2020, mainly related to UV, BCG vaccination, and other themes that could help to explain the slowdown of the pandemics in Mexico.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSlowdown of Infections in Mexico\u003c/p\u003e\n\u003cp\u003eFrom 78 hospitals designated as COVID-19 in CDMX (a low figure for the size of the city), 18 were still with places, according to the official site of the Mexican Government (Gobierno de la Ciudad de M\u0026eacute;xico 2020a). The average daily COVID cases for CDMX were 0.56 \u0026plusmn; 0.41 cases per 10ti, while LA had 2.42 \u0026plusmn; 1.33 cases per 10 ti, and NY had 2.35 \u0026plusmn; 2.11 cases per 10 ti (Fig. 1A). Total number of deaths in Mexico by June 23\u003csup\u003eth\u003c/sup\u003e was 22,580, a higher number in proportion to detected cases than most countries (Roser et al. 2020).\u003c/p\u003e\n\u003cp\u003eUV index and COVID relation\u003c/p\u003e\n\u003cp\u003eAverage UVI followed the opposite trend, with CDMX having the highest score (12.01 \u0026plusmn; 0.47), followed by LA (8.26 \u0026plusmn; 1.23) and NY (6.37 \u0026plusmn; 2.6) (Fig. 1B). The Adonis permutation test indicates significant differences among positive COVID cases numbers between the three cities (p= 0.0009) and UVI scores (p=0.0009). In addition, we found an independent effect of the UVI on the number of COVID cases between each city (p=0.0009, Table 2) and the effect of City and UV (P=0.0009). Quantile regression analysis shows a negative relation between COVID case number and the increase of UVI.\u0026nbsp; The pseudo R\u003csup\u003e1\u003c/sup\u003e value is increased in relation with quantile increase reaching a maximum R\u003csup\u003e1\u003c/sup\u003e of 0.36 for 90 and 95\u003csup\u003eth\u003c/sup\u003e quantiles (Table 3, Fig. 2).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGunthe et al. (2020) suggest a possible negative relationship between UV radiation, temperature, and COVID case numbers. However, their data are not conclusive because the adjusted curve diminishes on both sides around a peak, making it difficult to interpret. In China, Yao et al. (2020) found no relationship between average UVI and COVID case numbers, but in one region (Hubei), these authors show a possible negative correlation. Other unpublished studies have tried to resolve weather effects on a pandemic, with no conclusive results (Xu et al. 2020). Some possible bias in mentioned studies could be the use of daily UV averages. We used the maximum UVI per day (that goes from zero to maximum), producing a comprehensive relation between UVI and positive COVID-19 cases.\u003c/p\u003e\n\u003cp\u003eDespite the observed relation, it is important to remark that the UVI is only one part of the explanation to the low prevalence of COVID cases in CDMX compared to similar cities as NY and LA. Paradoxically, CDMX has ideal conditions for a rapid spread of any pandemic due to population density and other factors as high prevalence of chronic diseases and hotspots of people accumulation, as the subway system (Metro). In addition to the UV effect, it is necessary to consider other factors that can explain the behavior of the COVID epidemic in CDMX and most of the country. One factor related to high UVI, additional to latitude, is the altitude above sea level. In the case of the Mexican Plateau, where CDMX and other major cities such as Toluca, Puebla, Quer\u0026eacute;taro, and Morelia are found, the average elevation is \u0026ge;2000 masl. For example, the UVB levels in a station located about 60 km southeast of CDMX (Tlamacas, at 4000 masl) had a maximum average irradiance of 400 mW/m2 of UVB in March, with a minimum near 250 mW/m2 in January. Two other localities at the same latitude, but at sea level, have around 300 mW/m2 (Manzanillo and Veracruz), with a minimum of about 150 mW/m2 on the same dates (Valdes-Barron et al. 2013). \u0026nbsp;In addition to the amount of UV reaching the surface, the type of it also affects its capacity to inactivate pathogens. UV is found in the environment in three forms: UVA, UVB, and UVC. Each of these will reach the surface in different quantities after atmospheric filtration, so the type of UV reaching the surface (and subsequent germicidal effect) is also affected by altitude over sea level (Dwivedi et al. 2018).\u003c/p\u003e\n\u003cp\u003eThe effect of natural and artificial UV on virus inactivation has been extensively studied. For example, the influenza virus is inactivated by up to 99% if exposed to full sunlight during a single day in Mexico City, but this value lowers to 66% in the winter (Sagripanti and Lyte 2007; Sagripanti and Lytle 2020). Another study demonstrated that aerosolized MHV Coronavirus could be highly sensitive to 254 nm UV-C, but it is dependent on the culture medium (Walker and Ko 2007). The effect of the UV on viral inactivation is thought to be through uracil dimerization in the RNA (Woo et al. 2012). These authors also found that UVC inactivates a bacteriophage virus more efficiently in aerosols (with differences depending on the media) than droplets.\u003c/p\u003e\n\u003cp\u003eOther Factors Proposed to Explain the Slowdown\u003c/p\u003e\n\u003cp\u003eDifferences in the behavior of COVID-19 development in Mexico (and possibly other countries at a similar latitude) compared to more northern countries can only be partially explained by the UV effect and altitude.\u003c/p\u003e\n\u003cp\u003eWe suggest some other factors that need to be included in a more holistic model to explain the slowdown COVID-19 infection in Mexico and CDMX. A possible link between solar radiation-Vitamin-D production and the increase of immunological defenses against viruses (Grant 2008). Although controversial, the effect of vitamin D deserves further study because it seems to have a positive result against this type of infection (Kakodkar et al. 2020). Laird et al. (2020) suggest a positive relation between immune system and vitamin D regulating cytokine response to pathogens, fact confirmed by Daneshkhah et al. (2020)\u003c/p\u003e\n\u003cp\u003eAnother factor influencing the COVID-19 epidemic behavior in Mexico could be the vaccination scheme. Recently, researchers have suggested that Bacillus Calmette-Gu\u0026eacute;rin (BCG) vaccination against tuberculosis could diminish the spread of COVID-19 (Gupta 2020). This factor has three possible explanations: 1. The molecular similarity between BCG antigens and viral antigens. After vaccination, memory B and T cells recognize both BCG and respiratory pathogens; 2. BCG could lead to antigen-independent activation of bystander B and T cells; and 3. Long-term activation and reprogramming of innate immune cells called trained immunity. \u0026nbsp;Monocytes undergo epigenetic histone modifications at promoter sites of genes encoding inflammatory cytokines, leading to long-term changes in their ability to respond to novel stimuli (Redelman-Sidi 2020). This effect leads the activation of Th1 immunity and downregulation of Th2, and the consequent reduction of local tissue destruction by SARS-Cov-2 (Gupta 2020). Indeed, there appears to be a significantly lower prevalence of COVID-19 in countries where it is mandatory for BCG vaccination. In Mexico, massive vaccination with BCG started in 1951 and continues up to the present (Hurtado-Ochoterena and Mat\u0026iacute;as-Juan 2005).\u003c/p\u003e\n\u003cp\u003eIt is also essential to notice that most houses in Mexico City have no central air conditioning or insulation system, unlike the northern temperate regions with severe weather. As a result, Mexican houses are sunny and well ventilated. Th air currents inside houses do not allow small particles to remain inside rooms or houses for a long time, diminishing the probability of infection. This hypothesis was analyzed, albeit only in a small number of studies (Hobday and Dancer 2013). Despite the effect of sunlight inside houses is not currently known it is possible that play a similar role as in the environment, neutralizing virus particles by UV radiation. Additionally, a sunny house represents a key element in preventing depression (An et al. 2016; Fleming et al. 2018) reinforcing the immune system (Szałach et al. 2019; Cruz-Pereira et al. 2020).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimilarly, the primary modes of metropolitan public transportation in Mexico also lack air conditioning, including the vast subway system (Metro) of CDMX used by hundreds of thousand people daily. These transportation systems always operate with the windows open and air circulation inside tunnels is enhanced using ventilation system. Overall, the citizens of Mexico live under conditions less favorable to virus survival on outdoor/public surfaces.\u003c/p\u003e\n\u003cp\u003eAs we present along this paper, there are evidences that mentioned factors (UVI, vaccination scheme, airflow) could reduce the spread velocity of COVID-19 infection. However, no current models or data exist to explain how these factors affect the spread velocity of COVID-19 infection in large cities as CDMX. This city has a high density of population, with many people having pre-existing chronic diseases as diabetes, high blood pressure and obesity making them more susceptible to viral diseases (Gnatiuc et al. 2019; Rodr\u0026iacute;guez-Torres and Casas-Pati\u0026ntilde;o 2019)\u003c/p\u003e\n\u003cp\u003eWe do not know precisely how each factor mentioned here contributes to lessening the spread of COVID-19. Nevertheless, the possible synergy of all of them appears to slower this pandemic. Currently, no model exists that can take all these factors into account. We, therefore, consider the aforementioned ideas as requiring rigorous testing.\u003c/p\u003e\n\u003cp\u003eRemarks on Data Gathered\u003c/p\u003e\n\u003cp\u003eA common weakness of any model is the lack of data. In the case of COVID-19, we can be confident that this is the principal limitation in explaining the epidemic development in Mexico and elsewhere (Burdorf et al. 2020; Naud\u0026eacute; 2020). If we add the asymptomatic people not reported but still contagious (Bai et al. 2020), the uncertainty may be higher.\u0026nbsp; It is known that people\u0026rsquo;s behavior is related to COVID-19 spread rates (Van Bavel et al. 2020). However, in certain conditions it is tough to calculate this relation, which is another source of uncertainly in the prediction models.\u003c/p\u003e\n\u003cp\u003eIt has been suggested that the number of fatalities could be three times higher than it currently is in Mexico (Ahmed 2020). This figure would show 10-12,000 fatalities for the country and is currently not supported by hard data by May 8\u003csup\u003eth\u003c/sup\u003e. Except for Brazil, most Latin American countries are experiencing a slow spread of COVID-19. However, these data can be misleading due to a lack of testing, which is low in every Latin American country (Table 1). Despite a lack of testing, the pandemic's true extent will eventually become evident if COVID-19 cases overwhelm hospitals and funerary services, as they have in Ecuador (mainly in the city of Guayaquil).\u003c/p\u003e\n\u003cp\u003eWe presume this slowdown will slow the rate of infection but also make it continuous throughout the summer complicating the function of sanitary services, including hospitals. Mexican authorities are working to solve the lack of services and resources needed to confront a massive infection of COVID-19, but already several hospitals have become a point of infection due to the lack of materials and poor installations (Kitroeff and Villegas 2020).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eSingle parameter models can explain partially the complicated spread patterns of COVID-19 in different parts of the world. We expect that the hypotheses presented here and other efforts will help to establish the causes of the differential epidemic spread observed. Other authors have marginally discussed part of the ideas exposed here (Zhang et al. 2020), although their work is controversial.\u003c/p\u003e\n\u003cp\u003eFinally, we need to extend our scientific understanding of environmental effects, city infrastructure, population age, vaccination schemes, socioeconomic conditions, social and political behavior on COVID-19 epidemic dynamics. Otherwise, the decisions taken by the governments and people will be uninformed and potentially erroneous.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSean Prosser (University of Guelph) critically reviewed the manuscript and assisted us with the English version.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe authors declare they have no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAhmed A. (2020) Hidden Toll: Mexico Ignores Wave of Coronavirus Deaths in Capital. New York Times. https://www.nytimes.com/2020/05/08/world/americas/mexico-coronavirus-count.html Accessed 9 May 2020\u003c/p\u003e\n\u003cp\u003eAn M, Colarelli S M, O\u0026rsquo;Brien K, Boyajian M E (2016) Why we need more nature at work: Effects of natural elements and sunlight on employee mental health and work attitudes. PLoS One 1:e0155614. https://doi.org/10.1371/journal.pone.0155614\u003c/p\u003e\n\u003cp\u003eBai Y, Yao L S, Wei T, Tian F, Jin D Y, Chen L J, Wang M Y (2020) Presumed Asymptomatic Carrier Transmission of COVID-19. Jama-Journal Am Med Assoc. 323:1406\u0026ndash;1407. https://doi.org/10.1001/jama.2020.2565.\u003c/p\u003e\n\u003cp\u003eVan Bavel J J, Baicker K, Boggio P S, Capraro V, Cichocka A, Cikara M, Crockett M J, Crum A J, Douglas K M, Druckman J N (2020) Using social and behavioural science to support COVID-19 pandemic response. Nat Hum Behav. 4:460\u0026ndash;471. https://doi.org/10.1038/s41562-020-0884-z\u003c/p\u003e\n\u003cp\u003eBurdorf A, Porru F, Rugulies R (2020) The COVID-19 (Coronavirus) pandemic: consequences for occupational health. Scand J Work Environ Health. 46:229\u0026ndash;230. https://doi.org/10.5271/sjweh.3893\u003c/p\u003e\n\u003cp\u003eCenteno J E O. (2020) El coronavirus 19 (COVID 19) en M\u0026eacute;xico. Rev Chil Salud P\u0026uacute;blica. 24:72\u0026ndash;73.\u003c/p\u003e\n\u003cp\u003eColorado State University (2020) UV-B Monitoring and Research Program , Natural Resource Ecology Laboratory. https://uvb.nrel.colostate.edu/UVB/da_UvIndex.jsf. Accessed 5 July 2020\u003c/p\u003e\n\u003cp\u003eCONAPO (2010) DELIMITACI\u0026Oacute;N DE LAS ZONAS METROPOLITANAS DE M\u0026Eacute;XICO 2010. http://www.conapo.gob.mx/en/CONAPO/Zonas_metropolitanas_2010. Accessed 5 July 2020\u003c/p\u003e\n\u003cp\u003eCruz-Pereira J S, Rea K, Nolan Y M, O\u0026rsquo;Leary O F, Dinan T G, Cryan J F (2020) Depression\u0026rsquo;s unholy trinity: dysregulated stress, immunity, and the microbiome. Annu Rev Psychol. 71:49-78. https://doi.org/10.1146/annurev-psych-122216-011613\u003c/p\u003e\n\u003cp\u003eDaneshkhah A, Eshein A, Subramanian H, Roy H K, Backman V (2020) The Role of Vitamin D in Suppressing Cytokine Storm in COVID-19 Patients and Associated Mortality. medRxiv. https://doi.org/10.1101/2020.04.08.20058578.\u003c/p\u003e\n\u003cp\u003eDepartment of Public Health (2020) Statewide Statistics. 2020. \u003ca href=\"https://www.cdc.gov/mmwr/statehealth/index.html\"\u003ehttps://www.cdc.gov/mmwr/statehealth/index.html\u003c/a\u003e Accessed 25 Jun 2020\u003c/p\u003e\n\u003cp\u003eDwivedi A, Tripathi A K, Singh J, Pal M K (2018) Ultraviolet Radiation (UVR): An Introduction BT\u0026nbsp; - Photocarcinogenesis \u0026amp; Photoprotection. In (ed) Ray R S, Haldar C, Dwivedi A, Agarwal N, Singh J. Springer Singapore, Singapore pp 1-8.\u003c/p\u003e\n\u003cp\u003eFleming D, Grimes A, Lebreton L, Mar\u0026eacute; D, Nunns P (2018) Valuing sunshine. Reg Sci Urban Econ. 68:268\u0026ndash;276. https://doi.org/ 10.1016/j.regsciurbeco.2017.11.008\u003c/p\u003e\n\u003cp\u003eGnatiuc L, Alegre-D\u0026iacute;az J, Wade R, Ramirez-Reyes R, Tapia-Conyer R, Garcilazo-\u0026Aacute;vila A, Chiquete E, Gonz\u0026aacute;les-Carballo C, Solano-Sanchez M, Clarke R (2019) General and Abdominal Adiposity and Mortality in Mexico City: A Prospective Study of 150 000 Adults. Ann Intern Med. 171:397\u0026ndash;405. https://doi.irg/ doi: 10.7326/M18-3502\u003c/p\u003e\n\u003cp\u003eGobierno de la Ciudad de M\u0026eacute;xico (2020a) Capacity in general care beds in Covid-19 Hospitals. \u003ca href=\"https://hospitales.covid19.cdmx.gob.mx/public/hospitales/EstatusHospitales.xhtml\"\u003ehttps://hospitales.covid19.cdmx.gob.mx/public/hospitales/EstatusHospitales.xhtml\u003c/a\u003e. Accessed 4 July 2020\u003c/p\u003e\n\u003cp\u003eGobierno de la Ciudad de M\u0026eacute;xico (2020b) Radiaci\u0026oacute;n Solar (UVB). \u003ca href=\"http://www.aire.cdmx.gob.mx/default.php?opc=%27aKBhnmI=%27\u0026amp;opcion=bQ\"\u003ehttp://www.aire.cdmx.gob.mx/default.php?opc=%27aKBhnmI=%27\u0026amp;opcion=bQ\u003c/a\u003e== Accessed 31 June 2020\u003c/p\u003e\n\u003cp\u003eGobierno de M\u0026eacute;xico (2020) Covid-19 M\u0026eacute;xico. \u003ca href=\"https://coronavirus.gob.mx/datos/#DownZCSV\"\u003ehttps://coronavirus.gob.mx/datos/#DownZCSV\u003c/a\u003e Accessed 28 June 2020\u003c/p\u003e\n\u003cp\u003eGrant W B (2008) Hypothesis-ultraviolet-B irradiance and vitamin D reduce the risk of viral infections and thus their sequelae, including autoimmune diseases and some cancers. Photochem Photobiol. 84:356\u0026ndash;365. https://doi.org/10.1111/j.1751-1097.2007.00266.x.\u003c/p\u003e\n\u003cp\u003eGunthe S S, Swain B, Patra S S, Amte A (2020) On the global trends and spread of the COVID-19 outbreak: preliminary assessment of the potential relation between location-specific temperature and UV index. J Public Heal. 24:1-10. https://doi.org/10.1007/s10389-020-01279-y.\u003c/p\u003e\n\u003cp\u003eGupta A (2020) Is Immuno-modulation the Key to COVID-19 Pandemic? Indian J Orthop. 54:394-397. https://doi.org/10.1007/s43465-020-00121-7.\u003c/p\u003e\n\u003cp\u003eHobday R A, Dancer S J (2013) Roles of sunlight and natural ventilation for controlling infection: historical and current perspectives. J Hosp Infect. 84:271\u0026ndash;282. https://doi.org/10.1016/j.jhin.2013.04.011.\u003c/p\u003e\n\u003cp\u003eHurtado-Ochoterena C A, Mat\u0026iacute;as-Juan N A (2005) Historia de la vacunaci\u0026oacute;n en M\u0026eacute;xico. Rev Mex Pueric y Pediatr\u0026iacute;a. 13:47\u0026ndash;52.\u003c/p\u003e\n\u003cp\u003eKakodkar P, Kaka N, Baig M N (2020) A Comprehensive Literature Review on the Clinical Presentation, and Management of the Pandemic Coronavirus Disease 2019 (COVID-19). Cureus 12:e7560. https://doi.org/10.7759/cureus.7560.\u003c/p\u003e\n\u003cp\u003eKitroeff N, Villegas P (2020) \u0026lsquo;It\u0026rsquo;s Not the Virus\u0026rsquo;: Mexico\u0026rsquo;s Broken Hospitals Become Killers, Too. New York Times. \u003ca href=\"https://www.nytimes.com/2020/05/28/world/americas/virus-mexico-doctors.html\"\u003ehttps://www.nytimes.com/2020/05/28/world/americas/virus-mexico-doctors.html\u003c/a\u003e. Accessed 20 May 2020.\u003c/p\u003e\n\u003cp\u003eKoenker R. (2005) Quantile Regression in R: A Vignette. In: Kroenker R (ed) Quantile Regression. Econometric Society Monographs. Cambridge University Press, Cambridge, Pages 295\u0026ndash;316.\u003c/p\u003e\n\u003cp\u003eKoenker R, Hallock K F (2001) Quantile regression. J Econ Perspect. 15:143\u0026ndash;156. https://doi.org/10.1257/jep.15.4.143.\u003c/p\u003e\n\u003cp\u003eKoenker R, Machado J A F (1999) Goodness of fit and related inference processes for quantile regression. J Am Stat Assoc. 94:1296\u0026ndash;1310 https://doi.org/10.2307/2669943.\u003c/p\u003e\n\u003cp\u003eLaird E, Rhodes J, Kenny R A (2020) Vitamin D and inflammation: potential implications for severity of COVID-19. Irish med J. 113:P81.\u003c/p\u003e\n\u003cp\u003e\u0026Ntilde;amendys-Silva S A (2020) Respiratory support for patients with COVID-19 infection. Lancet Respir Med. 8:e18. https://doi.org/10.1016/S2213-2600(20)30110-7\u003c/p\u003e\n\u003cp\u003eNaud\u0026eacute; W (2020) Artificial intelligence vs COVID-19: limitations, constraints and pitfalls. Ai Soc., 1:1-5. https://doi.org/10.1007/s00146-020-00978-0\u003c/p\u003e\n\u003cp\u003eNYC Health Department (2020) COVID-19 Data. 2020. https://www1.nyc.gov/site/doh/covid/covid-19-data.page Accessed 27 Jun 2020\u003c/p\u003e\n\u003cp\u003eRamos C (2020) Covid-19: la nueva enfermedad causada por un coronavirus. Salud Publica Mex. 62:225\u0026ndash;227.\u003c/p\u003e\n\u003cp\u003eRedelman-Sidi G (2020) Could BCG be used to protect against COVID-19? Nat Rev Urol. 17:316-317. https://doi.org/10.1038/s41585-020-0325-9.\u003c/p\u003e\n\u003cp\u003eRodr\u0026iacute;guez-Torres A, Casas-Pati\u0026ntilde;o D (2019) Social determinants of obesity in Mexico. Rev Enfermer\u0026iacute;a del Inst Mex del Seguro Soc. 26:281\u0026ndash;290.\u003c/p\u003e\n\u003cp\u003eRoser M, Ritchie H, Ortiz-Ospina E, Hasell J (2020) Statistics and Research Coronavirus Pandemic (COVID-19). \u003ca href=\"https://ourworldindata.org/\"\u003ehttps://ourworldindata.org/\u003c/a\u003e Accessed 25 Jun 2020\u003c/p\u003e\n\u003cp\u003eSagripanti J L, Lyte D L (2007) Inactivation of Influenza Virus by Solar Radiation. Photochem Photobiol. 83:1278\u0026ndash;1282. https://doi.org/DOI: 10.1111 \u0026frasl; j.1751-1097.2007.00177.x.\u003c/p\u003e\n\u003cp\u003eSagripanti J, Lytle C D (2020) Estimated Inactivation of Coronaviruses by Solar Radiation With Special Reference to COVID‐19. Photochem Photobiol. Accepted Author Manuscript, https://doi.org/10.1111/php.13293\u003c/p\u003e\n\u003cp\u003eSzałach Ł P, Lisowska K A, Cubała W J (2019) The influence of antidepressants on the immune system. Arch Immunol Ther Exp (Warsz). 67:143\u0026ndash;151. https://doi.org/ 10.1007/s00005-019-00543-8\u003c/p\u003e\n\u003cp\u003eTheodoros V G, Sussman R A, Mier J M, Odriozola M D, Poulas K, Farsalinos K (2020) Characteristics and risk factors for COVID-19 diagnosis and adverse outcomes in Mexico. JAMA 323:2052\u0026ndash;2059. https://doi.org/10.1101/2020.06.04.20122481\u003c/p\u003e\n\u003cp\u003eValdes-Barron M, Pelaez-Chavez J C, Bonifaz-Alfonzo R, Riveros-Rosas D, Velasco-Herrera V, Estevez-Perez H (2013) UVB solar radiation climatology for Mexico. Geofis Int 52:31\u0026ndash;42. https://doi.org/10.1016/s0016-7169(13)71460-0.\u003c/p\u003e\n\u003cp\u003eWalker C M, Ko G (2007) Effect of ultraviolet germicidal irradiation on viral aerosols. Environ Sci Technol 41:5460\u0026ndash;5465. https://doi.org/10.1021/es070056u.\u003c/p\u003e\n\u003cp\u003eWeatherOnline Ltd (2020) Weather Online. 2020. https://www.weatheronline.co.uk/weather/maps/forecastmaps?LANG=en\u0026amp;CONT=mxmx\u0026amp;MAPS=uv\u0026amp;LOOP=0\u0026amp;LAND=__\u0026amp;MORE=1\u0026amp;UP=0\u0026amp;R=0\u0026amp;DAY=0 Accessed 30 June 2020\u003c/p\u003e\n\u003cp\u003eWoo M H, Grippin A, Anwar D, Smith T, Wu C Y, Wander J D (2012) Effects of Relative Humidity and Spraying Medium on UV Decontamination of Filters Loaded with Viral Aerosols. Appl Environ Microbiol 78:5781\u0026ndash;5787 https://doi.org/10.1128/aem.00465-12.\u003c/p\u003e\n\u003cp\u003eXu B, Gutierrez B, Mekaru S, Sewalk K, Goodwin L, Loskill A, Cohn E L, Hswen Y, Hill S C, Cobo M M, Zarebski A E, Li S R, Wu C H, Hulland E, Morgan J D, Wang L, O\u0026rsquo;Brien K, Scarpino S V, Brownstein J S, Pybus O G, Pigott D M, Kraemer M U G (2020) Epidemiological data from the COVID-19 outbreak, real-time case information. Sci Data 7. https://doi.org/10.1038/s41597-020-0448-0.\u003c/p\u003e\n\u003cp\u003eYao Y, Pan J H, Liu Z X, Meng X, Wang W D, Kan H D, Wang W B (2020) No association of COVID-19 transmission with temperature or UV radiation in Chinese cities Eur Respir J. 55: 2000517. https://doi.org/10.1183/13993003.00517-2020.\u003c/p\u003e\n\u003cp\u003eZhang R, Li Y, Zhang A L, Wang Y, Molina M J (2020) Identifying airborne transmission as the dominant route for the spread of COVID-19. Proc. Natl. Acad. Sci 117:14857-14863. https://doi.org/https://www.pnas.org/cgi/doi/10.1073/pnas.2009637117.\u003c/p\u003e"},{"header":"Tables","content":"\u003cp\u003eTABLE 1 Total COVID-19 tests per thousand people by June 8\u003csup\u003eth\u003c/sup\u003e or 9\u003csup\u003eth\u003c/sup\u003e in some countries of the Americas. *People tested; **Units unclear ***Samples tested \u003csup\u003e+\u003c/sup\u003eTests performed.\u003c/p\u003e\n\u003ctable style=\"width:306.6pt;border-collapse:collapse;border:none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87.4pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;height: 19.7pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003eCountry\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109.6pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;height: 19.7pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003eTotal Tests/1,000 persons\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109.6pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;height: 19.7pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003eDate of the last report (2020)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"border: none;padding: 0in 5.4pt;height: 19.3pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003eCanada\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border: none;padding: 0in 5.4pt;height: 19.3pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003e75.43\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border: none;padding: 0in 5.4pt;height: 19.3pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003eJul 2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"border: none;padding: 0in 5.4pt;height: 19.3pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003eUnited States\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border: none;padding: 0in 5.4pt;height: 19.3pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003e101.09\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border: none;padding: 0in 5.4pt;height: 19.3pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003eJul 2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"border: none;padding: 0in 5.4pt;height: 19.3pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003ePeru\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border: none;padding: 0in 5.4pt;height: 19.3pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003e7.76\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border: none;padding: 0in 5.4pt;height: 19.3pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003eJul 3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"border: none;padding: 0in 5.4pt;height: 19.3pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003eColombia\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border: none;padding: 0in 5.4pt;height: 19.3pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003e15.69\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border: none;padding: 0in 5.4pt;height: 19.3pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003eJul 2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"border: none;padding: 0in 5.4pt;height: 19.3pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003eEcuador\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border: none;padding: 0in 5.4pt;height: 19.3pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003e6.82\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border: none;padding: 0in 5.4pt;height: 19.3pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003eJul 2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"border: none;padding: 0in 5.4pt;height: 19.3pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003eMexico\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border: none;padding: 0in 5.4pt;height: 19.3pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003e4.04\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border: none;padding: 0in 5.4pt;height: 19.3pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003eJun 26\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 19.7pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003eBolivia\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 19.7pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003e6.63\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 19.7pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003eJul 1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cp\u003e\n\u003cp\u003eTABLE 2 ADONIS permutation test results for COVID cases number in New York, Los Angeles and Mexico Cities.\u003c/p\u003e\n\u003ctable style=\"border: none;border-collapse:collapse;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:77.75pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New 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style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:77.75pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003eError\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:48.35pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003e188\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:63.05pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003e52.48\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:63.05pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:63.05pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:63.05pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003e1.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:63.1pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='line-height:150%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cp\u003e\n\u003cp\u003eTABLE 3 Quantile regression estimates of B0 and B1, 95% confidence intervals for B1, pseudo R\u003csup\u003e1\u003c/sup\u003e and P for Ho: B1 = 0 for four regression quantiles between UVI and COVID positive cases per 10 thousand inhabitants in New York, Los Angeles and Mexico City.\u003c/p\u003e\n\u003ctable style=\"border: none;border-collapse:collapse;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85.65pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eT (quantile)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73.5pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eBo\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73.05pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eB\u003csub\u003e1\u003c/sub\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74.8pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e95% CI for B\u003csub\u003e1\u003c/sub\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67.85pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eR\u003csup\u003e1\u003c/sup\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66.55pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003ep\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85.65pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e20\u003csup\u003eth\u003c/sup\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73.5pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e1.82\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73.05pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026ndash;0.13\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74.8pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026ndash;0.16 \u0026nbsp;\u0026ndash; 0.12\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67.85pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e0.19\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66.55pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eP \u0026lt; \u0026nbsp;0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85.65pt;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e50\u003csup\u003eth\u003c/sup\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73.5pt;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e4.13\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73.05pt;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026ndash;0.30\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74.8pt;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026ndash;0.35 \u0026ndash;0.23\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67.85pt;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e0.23\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66.55pt;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eP \u0026lt; \u0026nbsp;0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85.65pt;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e90\u003csup\u003eth\u003c/sup\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73.5pt;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e9.33\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73.05pt;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e-0.66\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74.8pt;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026ndash;0.83 \u0026ndash;0.53\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67.85pt;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e0.36\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66.55pt;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eP \u0026lt; \u0026nbsp;0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85.65pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp 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transportation.","lastPublishedDoi":"10.21203/rs.3.rs-51450/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-51450/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eAim: \u003c/strong\u003eThe current paper aims to present and discuss some hypothesis about why in Mexico and in particular the Mexico City the epidemy of COVID-19 seems to be slower than other countries.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSubject and methods:\u003c/strong\u003e We compared daily UVI maximum and the reported COVID cases in Mexico City, New York, and Los Angeles and performed an ADONIS permutation test to look for significant differences between case numbers and its relationship with the UV index. We performed a quantile regression to explain the relation between UVI and daily COVID cases. We also investigated and analyzed other possible factors contributing to this slowdown because UVI cannot be the only explanation.\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u0026nbsp;\u003c/strong\u003eAlthough Mexican authorities have applied one of the smaller numbers of tests in the world, it is possible to identify a slowdown in Mexico's rate of infections. We found a negative and significant difference between the UV and registered cases among the three cities analyzed. However, this slowdown cannot be explained by a single factor. It is possible to hypothesize about the confluence of other factors as Vitamin D levels in Mexican population, lack of central air conditioning and the ventilation in houses and metropolitan transportation systems. Also, the Bacillus Calmette–Guérin vaccination could be part of the explanation of this phenomenon.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Single parameter models as solar irradiation can explain partially the complicated spread of COVID-19 in different parts of the world. However, it is necessary to extend our scientific understanding of environmental effects, city infrastructure, population age, vaccination schemes, socioeconomic conditions, social and political behavior on COVID-19 epidemic dynamics.\u003c/p\u003e","manuscriptTitle":"Why does the COVID-19 outbreak in Mexico seem to be slower? Some hypotheses for testing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-08-03 22:10:49","doi":"10.21203/rs.3.rs-51450/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fa3e2ce2-1fbb-4ec7-b7ee-1aa71c59be5e","owner":[],"postedDate":"August 3rd, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":223488,"name":"Epidemiology"}],"tags":[],"updatedAt":"2020-08-03T22:10:49+00:00","versionOfRecord":[],"versionCreatedAt":"2020-08-03 22:10:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-51450","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-51450","identity":"rs-51450","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}