No abstract

The relative risk of a disease is the observed probability that a member of an exposed group will develop the disease relative to the expected probability that a member of a susceptible group will develop the same disease. The estimation of relative risk is important for disease mapping; it is a method used to illustrate the geographical distribution of a disease occurrence for identifying areas that need more attention. Better estimates of risk would subsequently produce more accurate maps of disease risk. The study on relative risk estimation of leptospirosis in Malaysia is very scarce. Most of the related studies involved only the demographic of the disease. Furthermore, most of the mathematical modelling and statistical analyses used for disease transmission models have been deterministic; do not consider the potential of random effects. Thus, the objective of this study is to propose a discrete-time discrete-space stochastic model for relative risk estimation of leptospirosis in Malaysia based on a SIR-SI transmission model. The proposed model was demonstrated using Malaysia leptospirosis dataset (2012-2016) to estimate and analyse the expected relative risks of leptospirosis for all states. The results showed that the averages of estimated relative risks are between 0.340 and 2.898. Kelantan and Terengganu are the two most vulnerable states of leptospirosis for every epidemiology year from 2012 to 2016.

The disease leptospirosis is known to be endemic in Malaysia, and it significantly impacts human wellbeing and the national economy. Current surveillance systems are based on morbidity and mortality leptospirosis national data from the Ministry of Health and remain inadequate due to the number of unreported and misdiagnosed cases. A robust surveillance system is needed to monitor temporal and spatial changes which yield improvements in terms of identifying high-risk areas and disease behaviour. The objective of this study is to identify high-risk areas by estimating relative risk using existing models which are the Standardized Morbidity Ratio (SMR), Poisson-gamma, log-normal, Besag, York and Mollié (BYM) and mixture models. An alternative model is also proposed which involves transmission systems and stochastic elements, namely the stochastic Susceptible-Infected-Removed (SIR) transmission model. This estimation of risk is expected to assist in the early detection of high-risk areas which can be applied as a strategy for preventive and control measures. The methodology in this paper applies relative risk estimates to determine the infection risk for all states in Malaysia based on monthly data from 2011 to 2018 using WinBUGS 1.4 software. The results of relative risks are discussed and presented in tables and graphs for each model to disclose high-risk areas across the country. Based on the risk estimates, different models used have different risk interpretations and drawbacks which make each model different in its use depending on the objectives of the study. As a result, the deviance information criteria (DIC) values obtained do not differ greatly from each expected risk which was estimated