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Purpose <p>After the first cases of COVID-19 occurred in some metropolitan areas in Brazil, the virus spread to smaller cities connected to these centers, in a process of infection internalization. Models that explain this phenomenon can help in preparing necessary actions to prevent new cases. Therefore, this study presents a novel random variable that models the probability of delay, in days, of the first infection in a smaller city connected by commuting movement of people to an already infected metropolitan center, a city that already has a community transmission of the infection.</p> Methods <p>The new random variable and its probability distribution were formulated under general theoretical assumptions, while a methodology for their use was exemplified in four real scenarios referring to the cities of each of the following Brazilian states: Espírito Santo, Minas Gerais, Rio de Janeiro, and São Paulo.</p> Results <p>The study found a strong adherence of the new random variable’s distribution to real data, particularly in the states of Rio de Janeiro and São Paulo. The model also obtained good adherence with the data from the other two states, except for some points that were known to violate certain model assumptions.</p> Conclusion <p>The use of this variable can be an important tool for designing intervention strategies for geographic infection containment and/or efficient allocation of resources. The results confirm the usefulness of the new random variable in assessing the risk of the first imported infection to small cities in order to prevent possible future outbreaks of infectious diseases.</p>

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Modeling time to first COVID-19 infection in Brazilian cities connected to larger cities under exponential infection growth

  • 期刊论文

  • 发表日期：

    2025年1月31日

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  • Thiago Silva,
  • Patrick Ciarelli,
  • Jugurta Montalvão,
  • Evandro Salles

摘要

Purpose

After the first cases of COVID-19 occurred in some metropolitan areas in Brazil, the virus spread to smaller cities connected to these centers, in a process of infection internalization. Models that explain this phenomenon can help in preparing necessary actions to prevent new cases. Therefore, this study presents a novel random variable that models the probability of delay, in days, of the first infection in a smaller city connected by commuting movement of people to an already infected metropolitan center, a city that already has a community transmission of the infection.

Methods

The new random variable and its probability distribution were formulated under general theoretical assumptions, while a methodology for their use was exemplified in four real scenarios referring to the cities of each of the following Brazilian states: Espírito Santo, Minas Gerais, Rio de Janeiro, and São Paulo.

Results

The study found a strong adherence of the new random variable’s distribution to real data, particularly in the states of Rio de Janeiro and São Paulo. The model also obtained good adherence with the data from the other two states, except for some points that were known to violate certain model assumptions.

Conclusion

The use of this variable can be an important tool for designing intervention strategies for geographic infection containment and/or efficient allocation of resources. The results confirm the usefulness of the new random variable in assessing the risk of the first imported infection to small cities in order to prevent possible future outbreaks of infectious diseases.