Two waves of COIVD-19 in Brazilian cities and vaccination impact

Lixin Lin; Boqiang Chen; Yanji Zhao; Weiming Wang; Daihai He; Lixin Lin; Boqiang Chen; Yanji Zhao; Weiming Wang; Daihai He

doi:10.3934/mbe.2022216

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 5: 4657-4671. doi: 10.3934/mbe.2022216

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Research article

Two waves of COIVD-19 in Brazilian cities and vaccination impact

1.
Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong 999077, China
2.
School of Mathematics and Statistics, Huaiyin Normal University, Huaian 223300, China

Academic Editor: Yuan Lou

Received: 04 December 2021 Revised: 05 February 2022 Accepted: 22 February 2022 Published: 09 March 2022

Backgrounds
Brazil has suffered two waves of Coronavirus Disease 2019 (COVID-19). The second wave, coinciding with the spread of the Gamma variant, was more severe than the first wave. Studies have not yet reached a conclusion on some issues including the extent of reinfection, the infection fatality rate (IFR), the infection attack rate (IAR) and the effects of the vaccination campaign in Brazil, though it was reported that confirmed reinfection was at a low level.

Methods
We modify the classical Susceptible-Exposed-Infectious-Recovered (SEIR) model with additional class for severe cases, vaccination and time-varying transmission rates. We fit the model to the severe acute respiratory infection (SARI) deaths, which is a proxy of the COVID-19 deaths, in 20 Brazilian cities with the large number of death tolls. We evaluate the vaccination effect by a contrast of "with" vaccination actual scenario and "without" vaccination in a counterfactual scenario. We evaluate the model performance when the reinfection is absent in the model.

Results
In the 20 Brazilian cities, the model simulated death matched the reported deaths reasonably well. The effect of the vaccination varies across cities. The estimated median IFR is around 1.2%.

Conclusion
Overall, through this modeling exercise, we conclude that the effects of vaccination campaigns vary across cites and the reinfection is not crucial for the second wave. The relatively high IFR could be due to the breakdown of medical system in many cities.
- COVID-19,
- reinfection,
- breakthrough infection,
- vaccination effectiveness,
- mathematical modelling
Citation: Lixin Lin, Boqiang Chen, Yanji Zhao, Weiming Wang, Daihai He. Two waves of COIVD-19 in Brazilian cities and vaccination impact[J]. Mathematical Biosciences and Engineering, 2022, 19(5): 4657-4671. doi: 10.3934/mbe.2022216

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Abstract

Backgrounds

Brazil has suffered two waves of Coronavirus Disease 2019 (COVID-19). The second wave, coinciding with the spread of the Gamma variant, was more severe than the first wave. Studies have not yet reached a conclusion on some issues including the extent of reinfection, the infection fatality rate (IFR), the infection attack rate (IAR) and the effects of the vaccination campaign in Brazil, though it was reported that confirmed reinfection was at a low level.

Methods

We modify the classical Susceptible-Exposed-Infectious-Recovered (SEIR) model with additional class for severe cases, vaccination and time-varying transmission rates. We fit the model to the severe acute respiratory infection (SARI) deaths, which is a proxy of the COVID-19 deaths, in 20 Brazilian cities with the large number of death tolls. We evaluate the vaccination effect by a contrast of "with" vaccination actual scenario and "without" vaccination in a counterfactual scenario. We evaluate the model performance when the reinfection is absent in the model.

Results

In the 20 Brazilian cities, the model simulated death matched the reported deaths reasonably well. The effect of the vaccination varies across cities. The estimated median IFR is around 1.2%.

Conclusion

Overall, through this modeling exercise, we conclude that the effects of vaccination campaigns vary across cites and the reinfection is not crucial for the second wave. The relatively high IFR could be due to the breakdown of medical system in many cities.

References

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