The VMI is currently engaged in developing compartmental models and simulations of dengue trasnmission dynamics in the Mekong region in Southeast Asia. An excellent collaboration is ongoing with the Thai Ministry of Health that has incorporated computational modeling as tools in public health policy making. Aedes aegyptiThe VMI is developing additional partnerships in Laos, Cambodia and Vietnam to develop models for dengue transmission at country as well as regional level.

Tools are being developed in collaboration with the Thai Ministry of Health to enhance the dengue surveillance system by predicting future cases based on recent cases both in particular provinces and neighboring provinces. Using Hidden Markov Models, we have built a predictive system that can be used to predict future case incidence as well as indicate provinces that are experiencing an outbreak, or may be experiencing failures in surveillance. For outbreak detection and allocation of control and outbreak investigation resources, week by week and month by month, we have introduced the idea of monitoring reproductive numbers rather than simply case data. Reproductive numbers provide a better estimate of ongoing transmission than case numbers, which reflect the result of past transmission and past case numbers.

Picture of El Nino analysisA wavelet analysis of time series data from Thailand, Mexico and Puerto Rico was conducted, examining the relationship between El Niño and multi-annual variability in dengue incidence.  This analysis questioned several earlier analyses by others suggesting a strong link between dengue and El Niño.  This was an important step in assessing the impact of other mechanisms that might drive synchrony and temporal patterns in each location. We are currently analyzing the synchrony of long-term oscillations of incidence in Thailand and Malaysia, using wavelet decomposition. We are interested in extracting the phase structure of these cycles to see if there is evidence of traveling waves or spatial lags in phase across these two countries.  We are particularly interested in the border regions between these two countries.

Using data from each of the 72 provinces of Thailand, we looked for associations between the force of infection (a measure of hazard, defined as the rate per capita at which susceptible individuals become infected) and demographic and climactic variables. We estimated the force of infection from the age distribution of cases from 1985 to 2005. Contrary to recent findings suggesting that the incidence of DHF has increased in Thailand, we find a small but statistically significant decline in DHF incidence since 1985 in a majority of provinces. Thailand meetingThe strongest predictor of the change in force of infection and the mean force of infection is the median age of the population. Using mathematical simulations of dengue transmission we show that a reduced birth rate and a shift in the population’s age structure can explain the shift in the age distribution of cases, reduction of the force of infection, and increase in the periodicity of multiannual oscillations of DHF incidence in the absence of other changes.

We have developed an age specific model of dengue vaccine that is calibrated to data from Thailand.  The model structure is a compartmental age stratified model, with vaccination moving individuals from the susceptible and primary immune classes to susceptible, primary immunes or fully immune with some probability. We have chosen the parameters of the model so that it shows the same periodicity of multi-annual cycles as dengue incidence as characterized by Fourier spectra, and that it shows the same age distribution of cases from Thailand. We have used this model to compare different vaccination strategies by age and dosing.

Thailand water jarsWe are using this model to determine if there are vaccine product profiles that can lead to increases in secondary infections in particular age groups at particular times after the initiation of a mass immunization campaign.  We are using our estimates of the force of infection in Thailand (and other settings to be considered) to calculate the optimal scheduling of vaccine doses, critical vaccination fractions, and optimal spatial deployment of vaccines.

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