Vaccination efforts in Brazil: scenarios and perspectives under a mathematical modeling approach (preprint)

An agent-based model is proposed to access the impact of vaccination strategies to halt the COVID-19 spread. The model is parameterized using data from São Paulo State, Brazil. It was considered the two vaccines that are already approved for emergency use in Brazil, the CoronaVac vaccine developed by the Chinese bio-pharmaceutical company Sinovac and the Oxford-AstraZeneca vaccine (ChadOx1) developed by Oxford University and the British laboratory AstraZeneca. Both of them are two-dose schemes, but the efficacy and the interval between doses are different. We found that even in the worst scenario, in which the vaccine does not prevent infection either severe symptoms, the number of deaths decreases from 122 to 99 for CoronaVac application and to 80 for ChadOx1 administration. The same patterns have been seen in hospitalizations. Nevertheless, we show that when a low risk perception occurs, the reduction values decrease between 2% to 4%. Moreover, the increase of disease prevalence also jeopardizes immunization, showing the importance of the mitigation measures maintenance. On the other hand, doubling the vaccination rate would be able to significantly decrease the disease outcomes, reducing deaths by up to 74.4%. In conclusion, vaccination, along with non-pharmaceutical measures, is key to the control of COVID-19 in Brazil.

Impact of nonpharmaceutical governmental strategies for prevention and control of COVID-19 in Sao Paulo State, Brazil. (preprint)

Interrupted time series analyses (ITSA) were performed to measure the impact of social distancing policies (instituted 22/03/2020) and subsequent mandatory masking in the community (instituted 04/05/2020) on the incidence and effective reproductive number (Rt) of COVID-19 in Sao Paulo State, Brazil. Overall, the impact of social distancing both on incidence and Rt was greater than the incremental effect of mandatory masking. Those findings may reflect either a small impact of face masking or the loosening of social distancing after mandatory use of masks.

Elementary spatial structures and dispersion of COVID-19: health geography directing responses to public health emergency in Sao Paulo State, Brazil (preprint)

Public health policies to contain the spread of COVID-19 rely mainly on non-pharmacological measures. Those measures, especially social distancing, are a challenge for developing countries, such as Brazil. In Sao Paulo, the most populous state in Brazil (45 million inhabitants), most COVID-19 cases up to April 18th were reported in the Capital and metropolitan area. However, the inner municipalities, where 20 million people live, are also at risk. As governmental authorities discuss the loosening of measures for restricting population mobility, it is urgent to analyze the routes of dispersion of COVID-19 in those municipalities. In this ecological study, we use geographical models of population mobility as patterns for spread of SARS-Cov-2 infection. Based on surveillance data, we identify two patterns: one by contiguous diffusion from the capital metropolitan area and other that is hierarchical, with long-distance spread through major highways to cities of regional relevance. We also modelled the impact of social distancing strategies in the most relevant cities, and estimated a beneficial effect in each and every setting studied. This acknowledgement can provide real-time responses to support public health strategies.

Several countries in one: a mathematical modeling analysis for COVID-19 in inner Brazil (preprint)

Early 2020 and the world experiences its very first pandemic of globalized era. A novel coronavirus, SARS-Cov-2, is the causative agent of severe pneumonia and rapidly spread through many nations, crashing health systems. In Brazil, the emergence of local epidemics in major metropolitan areas is a concern. In a huge and heterogeneous country, with regional disparities and climate diversity, several factors can modulate the dynamics of COVID-19. What should be the scenario for an inner Brazil and what can we do to control infection transmission in each one of these locations? In this paper, a mathematical model was developed to simulate disease transmission among individuals in several scenarios, differing by the intensity and type of control measures. Mitigation strategies rely on social distancing of all individuals, and detection and isolation of infected ones. The model shows that control effort varies among cities. The social distancing is the most efficient method to control disease transmission but improving detection and isolation of infected individuals can help loosening this mitigation strategy.

Taking the Inner Route: Spatial and Demographic Factors Affecting Vulnerability to COVID-19 Among 604 Cities from Inner São Paulo State, Brazil (preprint)

Objectives: The impact of COVID-19 in metropolitan areas has been extensively studied. The geographic spread to smaller cities is of great concern and may follow hierarchical influence of urban centers. With that in mind, we investigated factors that affect vulnerability of inner municipalities in São Paulo State, Brazil, an area with 24 million inhabitants. Methods: Surveillance data for confirmed COVID-19 cases and admissions for severe acute respiratory disease (SARD) up to April 18th were recorded for each of 604 municipalities that lay outside São Paulo metropolitan area. Vulnerability was assessed in Multivariable models, including sociodemographic indexes, road distance to the State Capital and the municipalities classification proposed by the Brazilian Institute of Geography and Statistics. Municipalities of great regional relevance were used as reference category for that classification. The outcome of interest for Cox regression was having COVID-cases, with time counting from the first report in São Paulo State. For binomial negative regression models, the outcomes of interest were rates of confirmed COVID-19 cases and admissions for SARD.Results: A total of 198 (32.8%) municipalities had autochthonous COVID-19 cases. In Cox models, affected municipalities were likely to have greater population density (Hazard Ratio[HR] for each 100 inhabitants per square kilometer, 1.07; 95% Confidence Interval [CI], (1.05-1.10)), proportion of inhabitants in urban area (HR, 1.02; 95%CI, 1.00-1.04), higher human development index (HDI, HR for 1%, 1.06; 95%CI, 1.00-1.13) and Gini Index for Inequality of income (HR for 1%, 1.04, 95% CI, 1.00-1.07). On the other hand, distance from the Capital was protective (HR for each 100Km, 0.82; 95%CI, 0.74-0.90). The HR95%[95%CI] also varied negatively according to the categories of influence of major centers (0.41 [0.22-0.77], 0.16 [0.09-0.32], 0.07 [0.03-0.15]). The binomial negative regression models for COVID-19 incidence also detected positive association with population density (Incidence Rate Ratio[IRR], 1.13; 95%CI, 1.07-1.18) and proportion of urban population (IRR, 1.04; 95%CI, 1.01-1.05), protection for cities distant to the Capital (IRR=0.73; 95%CI, 0.68-0.81) and increasing negative association for categories of influence (0.19 [0.09-0.42], 0.07 [0.03-0.15] and 0.03 [0.02-0.08]). Similar findings were detected when we used SARD incidence as outcome.Conclusion: Municipalities with greater population, density and regional influence were more likely to be affected earlier and more intensely by COVID-19. Non-pharmacological measures should be strengthened in those areas of greater risk.