Potential dissemination of epidemics based on Brazilian mobile geolocation data. Part I: Population dynamics and future spreading of infection in the states of São Paulo and Rio de Janeiro during the pandemic of COVID-19

Abstract

Mobile geolocation data is a valuable asset in the assessment of movement patterns of a population. Once a highly contagious disease takes place in a location the movement patterns aid in predicting the potential spatial spreading of the disease, hence mobile data becomes a crucial tool to epidemic models. In this work, based on millions of anonymized mobile visits data in Brazil, we investigate the most probable spreading patterns of the COVID-19 within states of Brazil. The study is intended to help public administrators in action plans and resources allocation, whilst studying how mobile geolocation data may be employed as a measure of population mobility during an epidemic. The first part of the study focus on the states of São Paulo and Rio de Janeiro during the period of March 2020, when the disease first started to spread in these states. Metapopulation models for the disease spread were simulated in order to evaluate the risk of infection of each city within the states, by ranking them according to the time the disease will take to infect each city. We observed that, although the high risk regions are those closer to the capital cities, where the outbreak has started, there are also cities in the countryside with great risk.

Determinants of physical distancing during the covid-19 epidemic in Brazil: effects from mandatory rules, numbers of cases and duration of rules

“…in Brazil 9 . Data is aggregated into the 'social distancing index' , which is used here as a proxy for physical distancing, a method used in previous research 10,11 . The index has values expressed in percentages (in a scale of 0% to 100%), in which 100% is a hypothetical situation in which the whole population stays at home for a whole day.…”

Robot dance: a city-wise automatic control of Covid-19 mitigation levels

“…Thus, for sake of comparison, we will show three scenarios: the current social distancing protocol, optimal control taking into account the office data, and finally, optimal control taking into account sub notification. In all cases we use mobility matrices estimated from anonymized mobile phone data [8].…”

“…Population splitting from data: The fraction of the population S i j can be estimated using a matrix that describes the mobility between the cities made available in [8]. It has at each entry an estimate of p i j (t) = # daily accumulated percentage of inhabitants of i that travels to j.…”

The Neighborhood Contagion Focus as a Spatial Unit for Diagnosis and Epidemiological Action against COVID-19 Contagion in Urban Spaces: A Methodological Proposal for Its Detection and Delimitation

“…At a larger scale, GIS have been used to model the spread of coronavirus in several countries and large regional areas, such as East Asia [ 22 ], the United States [ 23 ], China [ 24 , 25 ], India [ 26 ], Pakistan [ 27 ] or Iran [ 28 ]. At a sub-national level, some works studied the Brazilian states of Sao Paulo, Rio de Janeiro [ 29 ], and Bahia [ 30 ], whereas at an urban scale, it may be highlighted the studies on social determinants of the expansion of COVID-19 carried out in Buenos Aires and Luján, in Argentina [ 31 ], Medellín and Cali, in Colombia [ 32 ], or Mexico D.F. [ 33 ], complemented with the observations on the epidemic and contagion relationships in the city provided by Hooper [ 34 ].…”