Despite seasonal cholera outbreaks in Bangladesh, little is known about the relationship between environmental conditions and cholera cases. We seek to develop a predictive model for cholera outbreaks in Bangladesh based on environmental predictors. To do this, we estimate the contribution of environmental variables, such as water depth and water temperature, to cholera outbreaks in the context of a disease transmission model. We implement a method which simultaneously accounts for disease dynamics and environmental variables in a Susceptible-Infected-Recovered-Susceptible (SIRS) model. The entire system is treated as a continuous-time hidden Markov model, where the hidden Markov states are the numbers of people who are susceptible, infected, or recovered at each time point, and the observed states are the numbers of cholera cases reported. We use a Bayesian framework to fit this hidden SIRS model, implementing particle Markov chain Monte Carlo methods to sample from the posterior distribution of the environmental and transmission parameters given the observed data. We test this method using both simulation and data from Mathbaria, Bangladesh. Parameter estimates are used to make short-term predictions that capture the formation and decline of epidemic peaks. We demonstrate that our model can successfully predict an increase in the number of infected individuals in the population weeks before the observed number of cholera cases increases, which could allow for early notification of an epidemic and timely allocation of resources.

Acute respiratory diseases are transmitted over networks of social contacts. Large-scale simulation models are used to predict epidemic dynamics and evaluate the impact of various interventions, but the contact behavior in these models is based on simplistic and strong assumptions which are not informed by survey data. These assumptions are also used for estimating transmission measures such as the basic reproductive number and secondary attack rates. Development of methodology to infer contact networks from survey data could improve these models and estimation methods. We contribute to this area by developing a model of within-household social contacts and using it to analyze the Belgian POLYMOD data set, which contains detailed diaries of social contacts in a 24-hour period. We model dependency in contact behavior through a latent variable indicating which household members are at home. We estimate age-specific probabilities of being at home and age-specific probabilities of contact conditional on two members being at home. Our results differ from the standard random mixing assumption. In addition, we find that the probability that all members contact each other on a given day is fairly low: 0.49 for households with two 0-5 year olds and two 19-35 year olds, and 0.36 for households with two 12-18 year olds and two 36+ year olds. We find higher contact rates in households with 2-3 members, helping explain the higher influenza secondary attack rates found in households of this size.

Many epidemic models approximate social contact behavior by assuming random mixing within mixing groups (e.g., homes, schools, and workplaces). The effect of more realistic social network structure on estimates of epidemic parameters is an open area of exploration. We develop a detailed statistical model to estimate the social contact network within a high school using friendship network data and a survey of contact behavior. Our contact network model includes classroom structure, longer durations of contacts to friends than non-friends and more frequent contacts with friends, based on reports in the contact survey. We performed simulation studies to explore which network structures are relevant to influenza transmission. These studies yield two key findings. First, we found that the friendship network structure important to the transmission process can be adequately represented by a dyad-independent exponential random graph model (ERGM). This means that individual-level sampled data is sufficient to characterize the entire friendship network. Second, we found that contact behavior was adequately represented by a static rather than dynamic contact network. We then compare a targeted antiviral prophylaxis intervention strategy and a grade closure intervention strategy under random mixing and network-based mixing. We find that random mixing overestimates the effect of targeted antiviral prophylaxis on the probability of an epidemic when the probability of transmission in 10 minutes of contact is less than 0.004 and underestimates it when this transmission probability is greater than 0.004. We found the same pattern for the final size of an epidemic, with a threshold transmission probability of 0.005. We also find random mixing overestimates the effect of a grade closure intervention on the probability of an epidemic and final size for all transmission probabilities. Our findings have implications for policy recommendations based on models assuming random mixing, and can inform further development of network-based models.

The understanding of immunological interactions among the four dengue virus (DENV) serotypes and their epidemiological implications is often hampered by the lack of individual-level infection history. Using a statistical framework that infers full infection history, we analyze a prospective pediatric cohort in Nicaragua to characterize how infection history modulates the risks of DENV infection and subsequent clinical disease. After controlling for age, one prior infection is associated with 54% lower, while two or more are associated with 91% higher, risk of a new infection, compared to DENV-naive children. Children >8 years old have 55% and 120% higher risks of infection and subsequent disease, respectively, than their younger peers. Among children with ≥1 prior infection, intermediate antibody titers increase, whereas high titers lower, the risk of subsequent infection, compared with undetectable titers. Such complex dependency needs to be considered in the design of dengue vaccines and vaccination strategies.

The Zika pandemic sparked intense interest in whether immune interactions among dengue virus serotypes 1 to 4 (DENV1 to -4) extend to the closely related Zika virus (ZIKV). We investigated prospective pediatric cohorts in Nicaragua that experienced sequential DENV1 to -3 (2004 to 2015), Zika (2016 to 2017), and DENV2 (2018 to 2020) epidemics. Risk of symptomatic DENV2 infection and severe disease was elevated by one prior ZIKV infection, one prior DENV infection, or one prior DENV infection followed by one ZIKV infection, compared with being flavivirus-naïve. By contrast, multiple prior DENV infections reduced dengue risk. Further, although high preexisting anti-DENV antibody titers protected against DENV1, DENV3, and ZIKV disease, intermediate titers induced by previous ZIKV or DENV infection enhanced future risk of DENV2 disease and severity, as well as DENV3 severity. The observation that prior ZIKV infection can modulate dengue disease severity like a DENV serotype poses challenges to development of dengue and Zika vaccines.

Dengue virus serotypes 1 to 4 (DENV1–4) and Zika virus (ZIKV) are mosquito-borne flaviviruses that induce both virus-specific and broadly reactive antibodies. A first DENV infection is thought to induce antibodies that wane over 2 years to titers that can subsequently enhance severe dengue disease. Secondary DENV infection with a different serotype is thought to induce stable, cross-serotype protective antibodies. Low dengue disease incidence after the recent Zika pandemic led to the hypothesis that ZIKV infection is also transiently cross protective. We investigated antibody kinetics in 4189 children up to 11 years after one and multiple DENV and ZIKV infections in longitudinal cohorts in Nicaragua. We used a DENV inhibition enzyme-linked immunosorbent assay (iELISA), which measures antibodies associated with protection against dengue and Zika disease and with enhancement of dengue disease severity. Unexpectedly, we found that overall DENV iELISA titers stabilized by 8 months after primary DENV infection to a half-life longer than a human life and waned, although gradually, after secondary DENV infection. Similarly, DENV iELISA titers were stable or rose after primary ZIKV infection but declined in individuals with histories of DENV and ZIKV infection. In contrast, kinetics of anti-ZIKV antibodies after ZIKV infection were similar regardless of prior DENV immunity. We observed heterogeneity in DENV iELISA titer, suggesting that individual antibody titer set point, rather than waning, is important for future dengue disease risk. Together, these findings change our understanding of anti-flavivirus antibody kinetics and have implications for measuring vaccine efficacy and for predicting future dengue and Zika outbreaks.

Dengue viruses (DENV1-4) are mosquito-borne flaviviruses estimated to cause up to ∼400 million infections and ∼100 million dengue cases each year. Factors that contribute to protection from and risk of dengue and severe dengue disease have been studied extensively but are still not fully understood. Results from Phase 3 vaccine efficacy trials have recently become available for one vaccine candidate, now licensed for use in several countries, and more Phase 2 and 3 studies of additional vaccine candidates are ongoing, making these issues all the more urgent and timely. At the "Summit on Dengue Immune Correlates of Protection", held in Annecy, France, on March 8-9, 2016, dengue experts from diverse fields came together to discuss the current understanding of the immune response to and protection from DENV infection and disease, identify key unanswered questions, discuss data on immune correlates and plans for comparison of results across assays/consortia, and propose a research agenda for investigation of dengue immune correlates, all in the context of both natural infection studies and vaccine trials.