There is a growing concern over the ongoing rabies epidemic in Sarawak that has remain unresolved ever since the outbreak began in July 2017. As of today, there has been 18 positive human rabies cases reported, which includes 17 fatalities, and one survivor who is now on life support after a severe neurological complications. Subsequently, the death rate now stands at approximately 94%. This paper is a preliminary report on the simulation of rabies transmission dynamics in Sarawak. At present, research is still lacking on the disease dynamics of rabies in Malaysia particularly in the state of Sarawak. We propose here a deterministic, compartmental model with SEIRS framework to fit actual data on the number of human infected rabies cases in Sarawak from June 2017 to January 2019. The simulation predicts that rabies in Sarawak will persist even with the current outbreak management and control efforts. Further, sensitivity analysis showed that dog vaccination rate is the most influential parameter and the basic reproduction number is estimated to be higher than 1. Henceforth, there is a need to increase the access to dog vaccines especially in remote rural areas with lack of health facilities. Our findings also suggest that controlling dog births could prevent the spread of rabies from perpetuating in the state. Neutering or using other fertility control methods would reduce the input of new susceptible domestic dogs into the population while Trap-Neuter-Vaccinate-Release (TNVR) method can be implemented to control new births of free-roaming strays. In summary, increasing the coverage of dog vaccination and reducing the number newborn dogs would be the more effective strategies to manage the current rabies outbreak in Sarawak.
Despite entering its fourth year, the rabies outbreak in the East Malaysian state of Sarawak has claimed another nine lives in 2020, culminating with a total of 31 laboratory-confirmed cases of human rabies as of 31st December 2020. One of the outbreak control challenges faced by the authorities within a previously rabies-free area, such as in the case of Sarawak, is the lack of information regarding possible starting sources, notably hotspot locations of the outbreak. Identification of potential high-risk areas for rabies infection is a sine qua non for effective disease interventions and control strategies. Motivated by this and in preparation for future similar incidents, this paper presented a preliminary study on rabies hotspot identification. The modelling approach adopted the bipartite network where the two disjoint sets of nodes are the Location node and Dog (Bite Cases) node. The formulation of the network followed closely the Bipartite Modeling Methodology Framework. Thorough model verification was done in an attempt to show that such problem domain can be modelled using the Bipartite Modeling approach.
In Malaysia, COVID-19 were first detected as imported cases on 25 January and as local infection on 4 February 2020. A surge of positive cases ensued by March 2020 which led to a series of countrywide containment and mitigation measures known as Movement Control Order (MCO). We study the direct effects of MCO on the course of epidemic by analyzing the cumulative and daily infection cases of COVID-19 up to 31 December 2020 in Malaysia and its states using piecewise linear regression and segment neighborhoods algorithm of change-point analysis, respectively. Through piecewise regression on nationwide cases, MCO were likely to almost flatten the epidemic curve in just one month after it was first initiated. While for stateswise cases, the average length of series of concave downward is six months before it turn to concave upward, indicating the period of which deceleration of new cases can be expected. However, the starting of this wave of COVID-19 can be relatively vary for three months in different states and federal territories. Together with change-point analysis on daily cases, the statewise epidemic phases could be subdivided into two to four regimes, whereby the majority of phase transitions fall in April and last quarter of 2020. Overall, the statistical modelling shows that the immediate effect of MCO appears to be effective.
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