RESUMEN

At the end of the year 2019, a virus named SARS-CoV-2 induced the coronavirus disease, which is very contagious and quickly spread around the world. This new infectious disease is called COVID-19. Numerous areas, such as the economy, social services, education, and healthcare system, have suffered grave consequences from the invasion of this deadly virus. Thus, a thorough understanding of the spread of COVID-19 is required in order to deal with this outbreak before it becomes an infectious disaster. In this research, the daily reported COVID-19 cases in 92 sub-districts in Johor state, Malaysia, as well as the population size associated to each sub-district, are used to study the propagation of COVID-19 disease across space and time in Johor. The time frame of this research is about 190 days, which started from August 5, 2021, until February 10, 2022. The clustering technique known as spatio-temporal clustering, which considers the spatio-temporal metric was adapted to determine the hot-spot areas of the COVID-19 disease in Johor at the sub-district level. The results indicated that COVID-19 disease does spike in the dynamic populated sub-districts such as the state's economic centre (Bandar Johor Bahru), and during the festive season. These findings empirically prove that the transmission rate of COVID-19 is directly proportional to human mobility and the presence of holidays. On the other hand, the result of this study will help the authority in charge in stopping and preventing COVID-19 from spreading and become worsen at the national level.

RESUMEN

Dengue fever is a mosquito-borne viral infection of humans caused by a virus of the Flaviviridae family. In Malaysia the annual incidence risk of dengue fever for the period 2000 to 2019 ranged from 30 to 390 cases per 100,000. The aim of this paper was to identify spatial, temporal and spatio-temporal clusters of dengue fever in Peninsular Malaysia for the period 2015 to 2017. Counts of confirmed incident cases of dengue fever for each of the 86 districts of Peninsular Malaysia for the period 1 January 2015 to 31 December 2017 (inclusive) and district-level census data allowed us to calculate the incidence rate of dengue fever, defined as the number of confirmed cases of dengue fever per 100,000 person-years at risk. We applied Kulldorff's cylindrical space-time scan statistic and Takahashi et al.'s prismatic space-time scan statistic to the data. We identified no major differences in the number and location of spatial clusters of dengue incidence for 2015, 2016 and 2017 using Kulldorff's and Takahashi et al.'s method. Spatio-temporal clusters of dengue occurred at several times throughout each year in various high population dense areas. These clusters not only included high population density districts but also their adjacent district neighbours. The temporal clustering of dengue cases during the monsoon season (mid September to late December each year) implies that there is a biologically plausible causal association between rainfall and the incidence of dengue. Identification of locations and time periods when the frequency of dengue is high allows Malaysian public health authorities to be more objective in their decision making around vector control and dengue public awareness campaigns. Future research will quantify the association between population density and rainfall on dengue incidence. This will allow health authorities to take a more proactive approach for dengue control.

Asunto(s)

Dengue , Animales , Análisis por Conglomerados , Dengue/epidemiología , Humanos , Incidencia , Malasia/epidemiología , Análisis Espacio-Temporal

RESUMEN

Spatio-temporal disease mapping models give a great worth in epidemiology, especially in describing the pattern of disease incidence across geographical space and time. This paper analyses the spatial and temporal variability of dengue disease rates based on generalized linear mixed models. For spatio-temporal study, the models incorporate spatially correlated random effects as well as temporal effects. In this study, two different spatial random effects are applied and compared. The first model is based on Leroux spatial model, while the second model is based on the stochastic partial differential equation approach. For the temporal effects, both models follow an autoregressive model of first-order model. The models are fitted within a hierarchical Bayesian framework with integrated nested Laplace approximation methodology. The main objective of this study is to compare both spatio-temporal models in terms of their ability in representing the disease phenomenon. The models are applied to weekly dengue fever data in Peninsular Malaysia reported to the Ministry of Health Malaysia in the year 2017 according to the district level.