Modelling the impact of school reopening and contact tracing strategies on Covid-19 dynamics in different epidemiologic settings in Brazil

Borges, Marcelo Eduardo; Ferreira, Leonardo Souto; Poloni, Silas; Bagattini, Ângela Maria; Franco, Caroline; Rosa, Michelle Quarti Machado da; Simón, Lorena; Camey, Suzi Alves; Kuchenbecker, Ricardo de Souza; Prado, Paulo Inácio; Filho, José Alexandre Felizola Diniz; Kraenkel, Roberto André; Coutinho, Renato Mendes; Toscano, Cristiana M.

doi:10.1016/j.gloepi.2022.100094

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…Moreover, our agent-based model captures individual heterogeneity in behaviors such as mask wearing and compliance with social distancing and risk factors such as age and comorbidity. Rather than approximating the impacts of NPIs as a single variable that changes the force of infection, a method commonly used in mathematical models for simplicity, 43 , 44 , 45 we separately model specific NPIs, including social distancing, face mask use, school closure, testing, contact tracing, and home quarantine.…”

Section: Discussionmentioning

confidence: 99%

Impact of Vaccination and Nonpharmaceutical Interventions With Possible COVID-19 Viral Evolutions Using an Agent-Based Simulation

Lee,

Zabinsky,

Wasserheit

et al. 2024

AJPM Focus

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Section: Discussionmentioning

confidence: 99%

Impact of Vaccination and Nonpharmaceutical Interventions With Possible COVID-19 Viral Evolutions Using an Agent-Based Simulation

Lee,

Zabinsky,

Wasserheit

et al. 2024

AJPM Focus

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“…Spatial analyses facilitate the determination of disease locations, assessment of healthcare facilities and services, and delineation of societal boundaries; therefore, they constitute fundamental elements of epidemiological and health studies (Arvin et al, 2023;Bratton & Wójcik, 2022;Krauss et al, 2023;Yao et al, 2023). The identification of these geographical areas and high-risk groups enables the selection of appropriate healthcare, medical, and social measures to mitigate the impact of these risk factors (Borges et al, 2022;Juneau et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Territorial patterns of COVID-19 in Iran

Aryan Kya

2024

ASPAL

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Motives: Spatial analysis has become an essential tool in understanding the underlying factors that contribute to the distribution of viral pandemics, diseases, injuries, and mortality patterns. By visualizing geographical data in spatial maps, researchers can identify local distribution patterns and potential drivers behind these patterns. In health and medical sciences, there has been a growing recognition that spatial analysis and mapping techniques are helpful in addressing various challenges related to the allocation of healthcare resource in both urban and rural areas. Aim: The objective of this study was to analyze the spatial distribution pattern of the COVID-19 pandemic and the Index of Proximity Distribution (IPD) across 31 provinces of Iran between February 2019 and February 2023. A two-stage sampling method combining convenience and cluster sampling was used to examine COVID-19 distribution patterns in 31 provinces of Iran between 22 February 2020 and 22 February 2023. COVID-19 and IPD data were collected as part of this panel study. Data were analyzed using t-tests, chi-square tests, and analysis of variance (ANOVA) in SPSS version 28 (α = 0.05). Subsequently, daily COVID-19 infection data for each province in the analyzed period were processed in ArcGIS software, and the spatial distribution pattern of the pandemic in Iran were visualized by point density analysis. Standard distance and standard deviation ellipse techniques were employed to assess the density or dispersion of infected individuals and to determine the spatial distribution pattern of COVID-19 in Iran. A spatial autocorrelation (Moran’s I) analysis was conducted to identify the spatial distribution pattern of COVID-19 in Iran. Additionally, distance-based spatial autocorrelation was used to examine the prevalence of COVID-19 infection across Iranian provinces. In a grouping analysis, 31 Iranian provinces were classified into five groups based on the number of COVID-19 cases, and spatial statistics were used to examine the prevalence of COVID-19 within each group. A hot spot analysis and a standard distance (SD) analysis were conducted to explore spatial correlations in the number of individuals affected by COVID-19 in each province. Results: Based on the Moran index, a random spatial pattern with a Z-Score of 1.485 was identified in March 2019, whereas a clustered distribution of COVID-19 with a Z-Score of 3.039 was determined in February 2023. The distance-based spatial autocorrelation analysis revealed a positive value of the Moran index (0.136627) at a distance of 383.3 kilometers from Tehran, which points to positive spatial autocorrelation and a higher number of COVID-19 cases in nearby regions. Conversely, the Moran index assumed a negative value of 0.040246 at a distance of 726.6 kilometers from Tehran, which suggests that the number of pandemic cases decreased over distance from Tehran. Moreover, based on the results of the hot spot analysis, Tehran province was identified as a hot cluster with a higher prevalence of COVID-19 cases in that region. In contrast, Bushehr province was classified as a cold cluster with a lower prevalence of COVID-19 cases in comparison with the surrounding regions. These findings provide valuable insights into the spatial distribution and clustering of COVID-19 cases in Iran. The shift from a random spatial pattern in 2019 to clustered distribution in 2023 indicates that the pandemic spread rate increased over time. The positive spatial autocorrelation near Tehran highlights the role of proximity and population movement in the transmission of the virus. Furthermore, the identification of hot spots and cold spots in a country can inform targeted interventions and resource allocation to effectively manage and control the pandemic. Overall, this study demonstrates the value of spatial analysis in identifying the spatial distribution patterns and the dynamics of the COVID-19 pandemic in Iran. The integration of spatial analysis techniques with epidemiological data contributes to a better understanding of spatial-temporal patterns, facilitates effective public health responses and resource allocation strategies. These findings contribute to the growing body of knowledge on the spatial epidemiology of COVID-19 and can aid in informing future preparedness and response efforts in Iran and other regions that face similar challenges.

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“…The SEIR modelling approach was used to estimate the progression of the COVID-19 pandemic and to discuss the control strategies when implementing social distancing, periods of school closures, and human mobility patterns measures [20][21][22][23][24][25][26][27][28][29][30] . The studies proposed measures to reduce the height of the peak to allow more time for health systems to expand and respond.…”

Section: Introductionmentioning

confidence: 99%

Preventable COVID-19 cases and deaths by alternative vaccination and non-pharmacological intervention policies in Brazil

Araújo,

Almeida,

Rodrigues

et al. 2023

Rev. bras. epidemiol.

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Objective: This work aimed to estimate the avoidable COVID-19 cases and deaths with the anticipation of vaccination, additional doses, and effective non-pharmacological interventions in Brazil. Methods: We developed a susceptible-exposed-infectious-recovered-susceptible model based on epidemiological indicators of morbidity and mortality derived from data obtained from the Health Information System of the Ministry of Health of Brazil. The number of cases and deaths was estimated for different scenarios of vaccination programs and non-pharmacological interventions in the states of Brazil (from March 8, 2020, to June 5, 2022). Results: The model-based estimate showed that 40 days of vaccination anticipation, additional vaccine doses, and a higher level the nonpharmacological interventions would reduce and delay the pandemic peak. The country would have 17,121,749 fewer COVID-19 cases and 391,647 avoidable deaths Conclusion: The results suggest that if 80% of the Brazilian population had been vaccinated by May 2021, 59.83% of deaths would have been avoided in Brazil.

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Modelling the impact of school reopening and contact tracing strategies on Covid-19 dynamics in different epidemiologic settings in Brazil

Cited by 4 publications

References 27 publications

Impact of Vaccination and Nonpharmaceutical Interventions With Possible COVID-19 Viral Evolutions Using an Agent-Based Simulation

Impact of Vaccination and Nonpharmaceutical Interventions With Possible COVID-19 Viral Evolutions Using an Agent-Based Simulation

Territorial patterns of COVID-19 in Iran

Preventable COVID-19 cases and deaths by alternative vaccination and non-pharmacological intervention policies in Brazil

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