A carregar...
Publicação
Spatiotemporal dynamics of epidemiology diseases: mobility based risk and short-term prediction modeling of COVID-19
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 5.53 MB | Adobe PDF |
Orientador(es)
Resumo(s)
Nowadays, epidemiological modeling is applied to a wide range of diseases,
communicable and non-communicable, namely AIDS, Ebola, influenza,
Dengue, Malaria, Zika. More recently, in the context of the last pandemic
declared by the World Health Organization (WHO), several studies applied these
models to SARS-CoV-2. Despite the increasing number of researches using
spatial analysis, some constraints persist that prevent more complex modeling
such as capturing local epidemiological dynamics or capturing the real patterns
and dynamics. For example, the unavailability of: (i) epidemiological information
such as the frequency with which it is made available; (ii) sociodemographic
and environmental factors (e.g., population density and population mobility) at
a finer scale which influence the evolution patterns of infectious diseases; or
(iii) the number of cases information that is also very dependent on the degree
of testing performed, often with severe territorial disparities and influenced by
context factors. Moreover, the delay in case reporting and the lack of quality
control in epidemiological information is responsible for biases in the data that
lead to many results obtained being subject to the ecological fallacy, making
it difficult to identify causal relationships. Other important methodological
limitations are the control of spatiotemporal dependence, management of
non-linearity, ergodicy, among others, which can impute inconsistencies
to the results. In addition to these issues, social contact, is still difficult to
quantify in order to be incorporated into modeling processes. This study aims
to explore a modeling framework that can overcome some of these modeling
methodological limitations to allow more accurate modeling of epidemiological
diseases. Based on Geographic Information Systems (GIS) and spatial analysis,
our model is developed to identify group of municipalities where population
density (vulnerability) has a stronger relationship with incidence (hazard) and
commuting movements (exposure). Specifically, our framework shows how
to operate a model over data with no clear trend or seasonal pattern which
is suitable for a short-term predicting (i.e., forecasting) of cases based on
few determinants. Our tested models provide a good alternative for when
explanatory data is few and the time component is not available, once they have
shown a good fit and good short-term forecast ability.
Descrição
Palavras-chave
Mobility Risk Non-linearity Ergodic Forecasting Simple exponential smoothing
Contexto Educativo
Citação
Silva, M., Viana, C. M., Betco, I., Nogueira, P., Roquette, R., & Rocha, J. (2024). Spatiotemporal dynamics of epidemiology diseases: mobility-based risk and short-term prediction modeling of COVID-19. Frontiers in Public Health, 12,1359167. https://doi.org/10.3389/fpubh.2024.1359167
Editora
Frontiers Media