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Advisor(s)
Abstract(s)
A COVID-19 foi declarada pela Organização Mundial de Saúde (OMS) como doença
pandémica a 12 de março de 2020. Atualmente a doença causada pelo SARS-CoV-2
continua a constituir um dos maiores problemas de saúde pública a nível mundial.
Apesar de terem sido desenvolvidas vacinas para o SARS-CoV-2, estas ainda não
garantem imunidade total à doença. Este trabalho de investigação tem como principal
objetivo auxiliar na tomada de decisão para combate ao SARS-CoV-2. Deste modo, é
fundamental a aplicação de métodos que possibilitem um melhor conhecimento sobre
os processos de difusão do vírus não só a nível espacial, como também a nível
espaciotemporal. Neste sentido, procurou-se compreender a distribuição espacial da
patologia COVID-19 em Portugal Continental a nível municipal e entender de que modo
a mobilidade humana influencia os processos de difusão do vírus; identificar os padrões
espaciotemporais de propagação; efetuar uma análise de risco através da determinação
da probabilidade de densidade de casos de infetados por SARS-CoV-2 por setor de
atividade; e determinar o possível comportamento futuro da incidência de casos. Assim,
verificou-se que a maioria dos hotspots dos casos de infetados por SARS-CoV-2 ocorrem
nos municípios das Áreas Metropolitanas (AMs). No entanto, após a aplicação de
medidas de restrição à mobilidade estes ocorrem em municípios do interior. No que diz
respeito à análise espaciotemporal, esta identificou a maioria dos municípios como
oscillating hotspots, o que corrobora a ideia apresentada anteriormente. A função
utilizada para definir a probabilidade de densidade de casos por setor de atividade
apresentou um bom ajuste. O modelo de predição do número de casos apresentou um
erro reduzido, e prevê que o número de casos continue a oscilar.
The World Health Organization (WHO) declared COVID-19 as a pandemic disease on 12th March 2020. Currently, the disease caused by SARS-CoV-2 remains one of the biggest public health problems in the world. Although a vaccine for SARS-CoV-2 has been developed, it does not yet guarantee total immunity to the disease. The main goal of this dissertation is to support decision-making in the fight against SARS-CoV-2. Thus, it is essential to apply methods that enable a better understanding of the virus dispersal, not only across space at a spatial level but also at the across space and time. In this regard, we analyzed the spatial distribution of COVID-19 in Portugal Continental at the municipality level to understand how human mobility influences the virus dispersal; identify spatiotemporal patterns of propagation; perform a risk analysis by estimating the density of individuals infected with SARS-CoV-2 by sector of activity; providing estimates of future incidence. It was found that SARS-CoV-2 hotspots occur generally in the municipalities of metropolitan areas. However, after the application of mobility restrictions, these occur mainly in countryside municipalities. On the spatiotemporal level, many municipalities were identified as oscillating hotspots, supporting the former result. The function used to define the probability of density of cases of infected individuals with SARS-CoV-2 by sector of activity shows a good adjustment and the forecasting model exhibits a reduced error and predicts that the number of cases will continue to oscillate.
The World Health Organization (WHO) declared COVID-19 as a pandemic disease on 12th March 2020. Currently, the disease caused by SARS-CoV-2 remains one of the biggest public health problems in the world. Although a vaccine for SARS-CoV-2 has been developed, it does not yet guarantee total immunity to the disease. The main goal of this dissertation is to support decision-making in the fight against SARS-CoV-2. Thus, it is essential to apply methods that enable a better understanding of the virus dispersal, not only across space at a spatial level but also at the across space and time. In this regard, we analyzed the spatial distribution of COVID-19 in Portugal Continental at the municipality level to understand how human mobility influences the virus dispersal; identify spatiotemporal patterns of propagation; perform a risk analysis by estimating the density of individuals infected with SARS-CoV-2 by sector of activity; providing estimates of future incidence. It was found that SARS-CoV-2 hotspots occur generally in the municipalities of metropolitan areas. However, after the application of mobility restrictions, these occur mainly in countryside municipalities. On the spatiotemporal level, many municipalities were identified as oscillating hotspots, supporting the former result. The function used to define the probability of density of cases of infected individuals with SARS-CoV-2 by sector of activity shows a good adjustment and the forecasting model exhibits a reduced error and predicts that the number of cases will continue to oscillate.
Description
Keywords
SARS-CoV-2 Análise espaciotemporal Mobilidade Predição