key: cord-1001209-ssfja1d5 authors: Carneiro, I. C.; Ferreira, E. D.; da Silva, J. C.; Soares, G.; Strottmann, D. M.; Silveira, G. F. title: Predictive model of COVID-19 incidence and socioeconomic description of municipalities in Brazil date: 2020-06-29 journal: nan DOI: 10.1101/2020.06.28.20141952 sha: cdeb749805f31cb6e42e38fd85fa028020437db7 doc_id: 1001209 cord_uid: ssfja1d5 Coronaviruses are enveloped viruses that can cause respiratory, gastrointestinal, hepatic, and neurological diseases. In December 2019, a new highly contagious coronavirus termed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in China. SARS-CoV-2 causes a potentially lethal human respiratory infection, COVID-19, that is associated with fever and cough and can progress to pneumonia and dyspnea in severe cases. Since the virus emerged, it has spread rapidly, reaching all continents around the world. A previous study has shown that, despite being the best alternative in the current pandemic context, social distancing measures alone may not be sufficient to prevent COVID-19 spread, and the overall impact of the virus is of great concern. The present study aims to describe the demographic and socioeconomic characteristics of 672 cities with cases of COVID-19, as well as to determine a predictive model for the number of cases. We analyzed data from cities with at least 1 reported case of COVID-19 until June 26, 2020. It was observed that cities with confirmed cases of the disease are present in all Brazilian states, affecting 36.5% of the municipalities in Rio de Janeiro State. The inhabitants in cities with reported cases of COVID-19 represent more than 73.1% of the Brazilian population. Stratifying the age groups of the inhabitants and accounting for the percentage of women and men does not affect COVID-19 incidence (confirmed cases/100,000 inhabitants). The demographic density, the MHDI and the per capita income of the municipalities with cases of COVID-19 do not affect disease incidence. In addition, if conditions are maintained, our model predicts 2,358,703 (2,172,930 to 2,544,477) cumulative cases on July 25, 2020. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 29, 2020. . https://doi.org/10.1101/2020.06. 28.20141952 doi: medRxiv preprint In the present work, an ecological study design was used; this method of 139 epidemiological study helped us to generate hypotheses about possible 140 associations between socioeconomic characteristics of the Brazilian 141 municipalities and the COVID-19 incidence and fatality rate. For the exploratory data analysis (EDA) and the predictive model (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 29, 2020. . https://doi.org/10.1101/2020.06.28.20141952 doi: medRxiv preprint (female/male) was also analyzed in cities with cases of COVID-19. In addition to 172 data from the population, the number of cities with COVID-19 cases within each between t and t−k when k=0, and therefore the PACF does not exist at lag 0. The ACF for an AR(p) process approaches zero very slowly, but the PACF goes 203 to zero for values of lag > p. The ACF for an MA(q) process goes to zero for 204 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 29, 2020. . https://doi.org/10.1101/2020.06.28.20141952 doi: medRxiv preprint values of lag > q, but the PACF approaches zero very slowly. As defined by the 205 ADF test, d = 2 was a second order of differentiation, making the series 206 stationary ( Figure 1G ). In the second order of differentiation, the PACF with lag 207 2 is already below significance ( Figure 1I ), p = 1. The same occurs in the ACF 208 in the second order, where lag 2 is below significance ( Figure 1H ). Then, the 209 parameters were tested by the minor Akaike Information Criteria (AIC), four 210 ARIMA models, (1,2,0), (1,2,1), (2,2,0), (2,2,1) and (2,2,2). With these data, the 211 ARIMA parameters (2,2,1) showed the best adjustment ( Figure 1J ). All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 29, 2020. . https://doi.org/10.1101/2020.06.28.20141952 doi: medRxiv preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 29, 2020. . https://doi.org/10.1101/2020.06.28.20141952 doi: medRxiv preprint ( Figure 3A ). The cities where cases of COVID-19 were observed have 238 153,528,953 inhabitants, representing 73.1% of the Brazilian population, and as 239 expected, the distribution of age groups is the same as the general distribution 240 in Brazil ( Figure 3B ). In the present study, the age ranges of the population in 241 the affected cities were grouped into seniors over age 65, with a higher risk; (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 29, 2020. . https://doi.org/10.1101/2020.06.28.20141952 doi: medRxiv preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 29, 2020. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 29, 2020. . https://doi.org/10.1101/2020.06.28.20141952 doi: medRxiv preprint 298 density do not have a greater or lesser incidence of COVID-19. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 29, 2020. The income was divided into low 1.4 to 3.8 (blue) and high 3.9 to 6.4 (orange). All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 29, 2020. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Academy of Sciences -PNAS: abr, 16, 2020. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 29, 2020. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 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