Modelling of Infectious Diseases for Providing Signal of Epidemics: A Measles Case Study in Bangladesh

The detection of unusual patterns in the occurrence of diseases is an important challenge to health workers interested in early identification of epidemics. The objective of this study was to provide an early signal of infectious disease epidemics by analyzing the disease dynamics. A two-stage monitoring system was applied, which consists of univariate Box-Jenkins model or autoregressive integrated moving average model and subsequent tracking signals from several statistical process-control charts. The analyses were illustrated on January 2000–August 2009 national measles data reported monthly to the Expanded Programme on Immunization (EPI) in Bangladesh. The results of this empirical study revealed that the most adequate model for the occurrences of measles in Bangladesh was the seasonal autoregressive integrated moving average (3, 1, 0) (0, 1, 1)12 model, and the statistical process-control charts detected no measles epidemics during September 2007–August 2009. The two-stage monitoring system performed well to capture the measles dynamics in Bangladesh without detection of an epidemic because of high measles-vaccination coverage

Dengue in Bangladesh: assessment of the influence of climate and under-reporting in national incidence

Dengue occurs in many tropical countries, despite substantial effort to control the Aedes mosquitoes that transmit the virus. The majority of the burden occurs in the South-East Asian Region of the World Health Organization. Bangladesh is a lower-middle income country located in South Asia, with strong seasonal weather variation, heavy monsoon rainfall, and high population density. Dengue has been endemic in Bangladesh since an epidemic in 2000. The aim of my research was to investigate the influence of climate on dengue transmission in Bangladesh over the period January, 2000 - December, 2009. To achieve this aim, I conducted a series of studies integrating epidemiological and socio-environmental factors into a unified statistical modelling framework to better understand transmission dynamics. In a narrative review (Chapter 3), I discuss the emergence and establishment of dengue along with the possibility of future epidemics of severe dengue. Introduction of a dengue virus strain from neighbouring Thailand likely caused the first epidemic in 2000. Cessation of dichlorodiphenyltrichloroethane (DDT) spraying, climatic, socio-demographic, and lifestyle factors also contributed to epidemic transmission and endemic establishment of the virus. However, there has been a decline in reported case numbers following the largest epidemic in 2002, albeit with relatively greater case numbers in alternate years. This occurred despite the absence of significant additional control measures and no changes in the surveillance system having been introduced during the study period. The observed decline from 2002 may be an artefact of the national hospital-based passive surveillance system even though a real decline in incidence could plausibly have occurred due to increased prevalence of immunity, greater public awareness, and reduced mosquito breeding sites. From a temporal negative binomial generalised linear model (Chapter 4), developed using monthly dengue cases in Dhaka from January, 2000 - December, 2009, I identify that mean monthly temperature (coefficient estimate: 6.07; 95% confidence interval: 3.38, 8.67) and diurnal temperature range (coefficient estimate: 15.57; 95% confidence interval: 8.03, 22.85) influence dengue transmission, with significant interaction between the two (coefficient estimate: -0.56; 95% confidence interval: -0.81, -0.29), at a lag of one month in Dhaka, the capital city of Bangladesh where the highest number of cases were reported during the study period. In addition to mean monthly rainfall in the previous two months, dengue incidence is associated with sea surface temperature anomalies in the current and previous months through concomitant anomalies in the annual rainfall cycle. Population density is also significantly associated with increased dengue incidence in Dhaka. Chapter 5 reports an investigation into non-linear dengue–climate associations using the same dataset as used for the previous model in Chapter 4. A Bayesian semi-parametric thin-plate spline approach estimates that the optimal mean monthly temperature for dengue transmission in Dhaka is 29oC and that average monthly rainfall above 15mm decreases transmission. This study also reveals that between 2000 and 2009 only 2.8% (95% Bayesian credible interval 2.7- 2.8) of cases estimated to have occurred in Dhaka were reported through passive case detection. A Bayesian spatio-temporal model (Chapter 6), formulated using monthly dengue cases reported across the country from January, 2000 - December, 2009, identifies that the majority of dengue cases occur in southern Bangladesh with the highest in Dhaka (located almost in the middle of the country), accounting for 93.0% of estimated total cases across the country from 2000-2009. Around 61.0% of Bangladeshi districts are identified as affected with dengue virus during the high transmission season of August and September, contrasting with national surveillance data suggesting that only 42.0% of districts are affected. My thesis provides a better understanding of the dengue-climate relationships that will enable more accurate predictions of the likely impacts of changing climate on dengue risk. Knowledge about the extent of under-reporting will facilitate precise estimation of dengue burden which is vital to assess the risk of severe epidemics. These will help public health professionals to design interventions to strengthen the country’s capacity for prevention of severe dengue epidemics

Modelling of Infectious Diseases for Providing Signal of Epidemics: A Measles Case Study in Bangladesh

The detection of unusual patterns in the occurrence of diseases is an important challenge to health workers interested in early identification of epidemics. The objective of this study was to provide an early signal of infectious disease epidemics by analyzing the disease dynamics. A two-stage monitoring system was applied, which consists of univariate Box-Jenkins model or autoregressive integrated moving average model and subsequent tracking signals from several statistical process-control charts. The analyses were illustrated on January 2000\u2013August 2009 national measles data reported monthly to the Expanded Programme on Immunization (EPI) in Bangladesh. The results of this empirical study revealed that the most adequate model for the occurrences of measles in Bangladesh was the seasonal autoregressive integrated moving average (3, 1, 0) (0, 1, 1)12 model, and the statistical process-control charts detected no measles epidemics during September 2007\u2013August 2009. The two-stage monitoring system performed well to capture the measles dynamics in Bangladesh without detection of an epidemic because of high measles-vaccination coverage

Restaurant Revenue Prediction Applying Supervised Learning Methods

In the competitive world, it is difficult to make a decision where to open a restaurant outlet that produces maximum revenue. Especially, it is difficult to accurately extrapolate across geographies and culture based on the personal judgement and experiences. Supervised learning approach may play a vital role to determine the feasibility of a new outlet with the prediction of revenue. The goal of this study was to predict restaurant revenue of 100,000 regional tab food investment (TFI) restaurant locations across Turkey. Several supervised learning techniques were used to select the optimal model for prediction. The LASSO method was selected as the best supervised method for the prediction of revenue as determined by lowest test error. Other models were employed, but LASSO outperformed all other models and had the added benefit of simplicity and interpretability. The LASSO model was used to predict the revenue of 100,000 new restaurant site locations based on the coefficients termed using the training data

Geostatistical mapping of the seasonal spread of under-reported dengue cases in Bangladesh

Geographical mapping of dengue in resource-limited settings is crucial for targeting control interventions but is challenging due to the problem of zero-inflation because many cases are not reported. We developed a negative binomial generalised linear mixed effect model accounting for zero-inflation, spatial, and temporal random effects to investigate the spatial variation in monthly dengue cases in Bangladesh. The model was fitted to the district-level (64 districts) monthly reported dengue cases aggregated over the period 2000 to 2009 and Bayesian inference was performed using the integrated nested Laplace approximation. We found that mean monthly temperature and its interaction with mean monthly diurnal temperature range, lagged by two months were significantly associated with dengue incidence. Mean monthly rainfall at two months lag was positively associated with dengue incidence. Densely populated districts and districts bordering India or Myanmar had higher incidence than others. The model estimated that 92% of the annual dengue cases occurred between August and September. Cases were identified across the country with 94% in the capital Dhaka (located almost in the middle of the country). Less than half of the affected districts reported cases as observed from the surveillance data. The proportion reported varied by month with a higher proportion reported in high-incidence districts, but dropped towards the end of high transmission season.SS was supported by The Australian National University Higher Degree Research Merit Scholarship (http://www.anu.edu.au/students/ scholarships-support/anu-university-researchscholarships

A stochastic model for early identification of infectious disease epidemics with application to measles cases in Bangladesh

In this article, a stochastic modeling approach was employed for the detection of epidemics in advance that was based on a negative binomial model with 2 components: an endemic component and an epidemic component. This study used monthly measles cases from January 2000 to August 2009 collected from the Expanded Program on Immunization, Bangladesh. General optimization routines provided the maximum likelihood estimates with corresponding standard errors. The negative binomial model with both seasonal endemic and epidemic components was shown to provide adequate fit with no measles epidemic during September 2008 to August 2009

A Bayesian approach for estimating underreported dengue incidence with a focus on non-linear associations between climate and dengue in Dhaka, Bangladesh

Determining the relation between climate and dengue incidence is challenging due to under-reporting of disease and consequent biased incidence estimates. Non-linear associations between climate and incidence compound this. Here, we introduce a modelling framework to estimate dengue incidence from passive surveillance data while incorporating non-linear climate effects. We estimated the true number of cases per month using a Bayesian generalised linear model, developed in stages to adjust for under-reporting. A semi-parametric thin-plate spline approach was used to quantify non-linear climate effects. The approach was applied to data collected from the national dengue surveillance system of Bangladesh. The model estimated that only 2.8% (95% credible interval 2.7–2.8) of all cases in the capital Dhaka were reported through passive case reporting. The optimal mean monthly temperature for dengue transmission is 29℃ and average monthly rainfall above 15 mm decreases transmission. Our approach provides an estimate of true incidence and an understanding of the effects of temperature and rainfall on dengue transmission in Dhaka, Bangladesh.The first author was supported by The Australian National University Higher Degree Research Merit Scholarship (http:// www.anu.edu.au/students/scholarships-support/anu-university-research-scholarships)

The emergence of dengue in Bangladesh: epidemiology, challenges and future disease risk

Dengue occurred sporadically in Bangladesh from 1964 until a large epidemic in 2000 established the virus. We trace dengue from the time it was first identified in Bangladesh and identify factors favourable to future dengue haemorrhagic fever epidemics. The epidemic in 2000 was likely due to introduction of a dengue virus strain from a nearby endemic country, probably Thailand. Cessation of dichlorodiphenyltrichloroethane (DDT) spraying, climatic, socio-demographic, and lifestyle factors also contributed to epidemic transmission. The largest number of cases was notified in 2002 and since then reported outbreaks have generally declined, although with increased notifications in alternate years. The apparent decline might be partially due to public awareness with consequent reduction in mosquito breeding and increased prevalence of immunity. However, passive hospital-based surveillance has changed with mandatory serological confirmation now required for case reporting. Further, a large number of cases remain undetected because only patients with severe dengue require hospitalisation. Thus, the reduction in notification numbers may be an artefact of the surveillance system. Indeed, population-based serological survey indicates that dengue transmission continues to be common. In the future, the absence of active interventions, unplanned urbanisation, environmental deterioration, increasing population mobility, and economic factors will heighten dengue risk. Projected increases in temperature and rainfall may exacerbate this

Interaction of mean temperature and daily fluctuation influences dengue incidence in Dhaka, Bangladesh

Local weather influences the transmission of the dengue virus. Most studies analyzing the relationship between dengue and climate are based on relatively coarse aggregate measures such as mean temperature. Here, we include both mean temperature and daily fluctuations in temperature in modelling dengue transmission in Dhaka, the capital of Bangladesh. We used a negative binomial generalized linear model, adjusted for rainfall, anomalies in sea surface temperature (an index for El Niño-Southern Oscillation), population density, the number of dengue cases in the previous month, and the long term temporal trend in dengue incidence. In addition to the significant associations of mean temperature and temperature fluctuation with dengue incidence, we found interaction of mean and temperature fluctuation significantly influences disease transmission at a lag of one month. High mean temperature with low fluctuation increases dengue incidence one month later. Besides temperature, dengue incidence was also influenced by sea surface temperature anomalies in the current and previous month, presumably as a consequence of concomitant anomalies in the annual rainfall cycle. Population density exerted a significant positive influence on dengue incidence indicating increasing risk of dengue in over-populated Dhaka. Understanding these complex relationships between climate, population, and dengue incidence will help inform outbreak prediction and control