OVID-19 is a severe acute respiratory infection (SARI) that emerged in early December 2019 in Wuhan, China 1. The outbreak was declared a public health emergency of international concern by the World Health Organization on 30 January 2020. COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), an enveloped, single-stranded positive-sense RNA virus that belongs to the Betacoronavirus genus and Coronaviridae family 2. SARS-CoV-2 is closely related genetically to bat-derived SARS-like coronaviruses 3. Human-to-human transmission occurs primarily via respiratory droplets and direct contact, similar to human influenza viruses, SARS-CoV and Middle East respiratory syndrome coronavirus 4. The most commonly reported clinical symptoms are fever, dry cough, fatigue, dyspnoea, anosmia, ageusia, or some combination of these 1,4,5. As of 16 June 2020, more than 7.9 million cases have been confirmed worldwide, resulting in 434,796 deaths 6. Brazil declared COVID-19 a national public health emergency on 3 February 2020 7. After the development of a national emergency plan and the early establishment of molecular diagnostic facilities across Brazil's network of public health laboratories, the country reported its first confirmed COVID-19 case on 25 February 2020, in a traveller returning to São Paulo from northern Italy 8. São Paulo is the largest city in South America and no other Brazilian city receives a greater proportion of international flights 9. Currently, Brazil has one of the fastest-growing COVID-19 epidemics in the world, now accounting for 1,864,681 cases and 72,100 deaths, comprising over 55% of the total number of reported cases in Latin America and the Caribbean (as of 14 July 2020) 6. About 21% of Latin American and Caribbean populations are estimated to be at risk of severe COVID-19 illness 10. The region has been experiencing large outbreaks, with growing epidemics in Brazil,

In January 2020 China reported to the World Health Organization an outbreak of pneumonia of undetermined origin in the city of Wuhan, Hubei. In January 30, 2020, the World Health Organization declared the outbreak of COVID-19 as a Public Health Emergency of International Interest (PHEI). Objectives: The aim of this study is to assess the impact of a COVID-19 epidemic in the metropolitan region of Sao Paulo, Brazil. Methods: We used a generalized SEIR (Susceptibles, Exposed, Infectious, Recovered) model, with additional Hospitalized variables (SEIHR model) and age-stratified structure to analyze the expected time evolution during the onset of the epidemic in the metropolitan area of Sao Paulo. The model allows to determine the evolution of the number of cases, the number of patients admitted to hospitals and deaths caused by COVID-19. In order to investigate the sensibility of our results with respect to parameter estimation errors we performed Monte Carlo analysis with 100 000 simulations by sampling parameter values from an uniform distribution in the confidence interval. Results: We estimate 1 368 (IQR: 880, 2 407) cases, 301 (22%) in older people (more than 60 years), 81 (50, 143) hospitalizations, and 14 (9, 26) deaths in the first 30 days, and 38 583 (IQR: 16 698, 113, 163) cases, 8 427 (21.8%) in older people (more than 60 years), 2181 (914, 6392) hospitalizations, and 397(166, 1205) deaths in the first 60 days. Limitations: We supposed a constant transmission probability Pc among different age-groups, and that every severe and critic case will be hospitalized, as well as that the detection capacity in all the primary healthcare services does not change during the outbreak. Conclusion: Supposing the reported parameters in the literature apply in the city of Sao Paulo, our study shows that it is expected that the impact of a COVID-19 outbreak will be important, requiring special planning from the authorities. This is the first study for a major metropolitan center in the south hemisphere, and we believe it can provide policymakers with a prognosis of the burden of the pandemic not only in Brazil, but also in other tropical zones, allowing to estimate total cases, hospitalization, and deaths, in support to the management of the public health emergence caused by COVID-19.

word count: 44 Article word count: 1113 . CC-BY-NC 4.0 International license It is made available under ais the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) AbstractWe calculated the impact of early social distancing on the COVID-19 transmission in the São Paulo metropolitan area and forecasted the ICU beds needed to cope the epidemic demand by using an age-stratified SEIR model. Within 60 days, these measures would avoid 89,133 deaths.

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Social distancing measures have emerged as the predominant intervention for containing the spread of COVID-19, but evaluating adherence and effectiveness remains a challenge. We assessed the relationship between aggregated mobility data collected from mobile phone users and the time-dependent reproduction number R(t), using severe acute respiratory illness (SARI) cases reported by Sao Paulo and Rio de Janeiro. We found that the proportion of individuals staying home all day (isolation index) had a strong inverse correlation with R(t) (rho<-0.7) and was predictive of COVID-19 transmissibility (p<0.0001). Furthermore, indexs of 46.7% had the highest accuracy (93.9%) to predict R(t) below one. This metric can be monitored in real time to assess adherence to social distancing measures and predict their effectiveness for controlling SARS-CoV-2 transmission.

Background: COVID-19 diagnosis is a critical problem, mainly due to the lack or delay in the test results. We aimed to obtain a model to predict SARS-CoV-2 infection in suspected patients reported to the Brazilian surveillance system. Methods: We analyzed suspected patients reported to the National Surveillance System that corresponded to the following case definition: patients with respiratory symptoms and fever, who traveled to regions with local or community transmission or who had close contact with a suspected or confirmed case. Based on variables routinely collected, we obtained a multiple model using logistic regression. The area under the receiver operating characteristic curve (AUC) and accuracy indicators were used for validation. Results: We described 1468 COVID-19 cases (confirmed by RT-PCR) and 4271 patients with other illnesses. With a data subset, including 80% of patients from Sao Paulo (SP) and Rio Janeiro (RJ), we obtained a function which reached an AUC of 95.54% (95% CI: 94.41% -96.67%) for the diagnosis of COVID-19 and accuracy of 90.1% (sensitivity 87.62% and specificity 92.02%). In a validation dataset including the other 20% of patients from SP and RJ, this model exhibited an AUC of 95.01% (92.51% -97.5%) and accuracy of 89.47% (sensitivity 87.32% and specificity 91.36%). Conclusion: We obtained a model suitable for the clinical diagnosis of COVID-19 based on routinely collected surveillance data. Applications of this tool include early identification for specific treatment and isolation, rational use of laboratory tests, and input for modeling epidemiological trends.

Purpose of ReviewThe purpose of this review is using the currently available clinical and epidemiological data, to identify key aspects to improve both the clinical management and public health response with regard SARS-CoV-2/HIV co-infection among HIV vulnerable populations and people living with HIV (PLWH).Recent Findings While at the beginning of the COVID-19 pandemic, the lack of robust information on SARS-CoV-2/HIV coinfection prevented to have a clear picture of the synergies between them, currently available data strongly supports the importance of common structural factors on both the acquisition and clinical impact of these infections and the relevance of age, co-morbidities, and HIV viral load as associated worse prognosis factors among PLWH. Summary Although more information is needed to better understand the biological, clinical, and epidemiological relationship between both infections, in the meanwhile, syndemic approaches to prevent SARS-CoV-2 among HIV higher risk groups and PLWH, targeting these population for SARS-CoV-2 vaccines and protocolizing early identification of HIV + patients with worse COVID-19 prognosis factors, are crucial strategies to decrease the overall impact of SARS-CoV-2 /HIV coinfection.

Objective COVID‐19 diagnosis is a critical problem, mainly due to the lack or delay in the test results. We aimed to obtain a model to predict SARS‐CoV‐2 infection in suspected patients reported to the Brazilian surveillance system. Methods We analysed suspected patients reported to the National Surveillance System that corresponded to the following case definition: patients with respiratory symptoms and fever, who travelled to regions with local or community transmission or who had close contact with a suspected or confirmed case. Based on variables routinely collected, we obtained a multiple model using logistic regression. The area under the receiver operating characteristic curve (AUC) and accuracy indicators were used for validation. Results We described 1468 COVID‐19 cases (confirmed by RT‐PCR) and 4271 patients with other illnesses. With a data subset including 80% of patients from Sao Paulo (SP) and Rio Janeiro (RJ), we obtained a function which reached an AUC of 95.54% (95% CI: 94.41–96.67%) for the diagnosis of COVID‐19 and accuracy of 90.1% (sensitivity 87.62% and specificity 92.02%). In a validation dataset including the other 20% of patients from SP and RJ, this model exhibited an AUC of 95.01% (92.51–97.5%) and accuracy of 89.47% (sensitivity 87.32% and specificity 91.36%). Conclusion We obtained a model suitable for the clinical diagnosis of COVID‐19 based on routinely collected surveillance data. Applications of this tool include early identification for specific treatment and isolation, rational use of laboratory tests, and input for modelling epidemiological trends.