Fine-scale heterogeneity in population density predicts wave dynamics in dengue epidemics.

The spread of dengue and other arboviruses constitutes an expanding global health threat. The extensive heterogeneity in population distribution and potential complexity of movement in megacities of low and middle-income countries challenges predictive modeling, even as its importance to disease spread is clearer than ever. Using surveillance data at fine resolution from Rio de Janeiro, we document a scale-invariant pattern in the size of successive epidemics following DENV4 emergence. Using surveillance data at fine resolution following the emergence of the DENV4 dengue serotype in Rio de Janeiro, we document a pattern in the size of successive epidemics that is invariant to the scale of spatial aggregation. This pattern emerges from the combined effect of herd immunity and seasonal transmission, and is strongly driven by variation in population density at sub-kilometer scales. It is apparent only when the landscape is stratified by population density and not by spatial proximity as has been common practice. Models that exploit this emergent simplicity should afford improved predictions of the local size of successive epidemic waves.

The interplay between vaccination and social distancing strategies affects COVID19 population-level outcomes.

Social distancing is an effective population-level mitigation strategy to prevent COVID19 propagation but it does not reduce the number of susceptible individuals and bears severe social consequences-a dire situation that can be overcome with the recently developed vaccines. Although a combination of these interventions should provide greater benefits than their isolated deployment, a mechanistic understanding of the interplay between them is missing. To tackle this challenge we developed an age-structured deterministic model in which vaccines are deployed during the pandemic to individuals who do not show symptoms. The model allows for flexible and dynamic prioritization strategies with shifts between target groups. We find a strong interaction between social distancing and vaccination in their effect on the proportion of hospitalizations. In particular, prioritizing vaccines to elderly (60+) before adults (20-59) is more effective when social distancing is applied to adults or uniformly. In addition, the temporal reproductive number Rt is only affected by vaccines when deployed at sufficiently high rates and in tandem with social distancing. Finally, the same reduction in hospitalization can be achieved via different combination of strategies, giving decision makers flexibility in choosing public health policies. Our study provides insights into the factors that affect vaccination success and provides methodology to test different intervention strategies in a way that will align with ethical guidelines.

Predicting re-emergence times of dengue epidemics at low reproductive numbers: DENV1 in Rio de Janeiro, 1986-1990.

Predicting arbovirus re-emergence remains challenging in regions with limited off-season transmission and intermittent epidemics. Current mathematical models treat the depletion and replenishment of susceptible (non-immune) hosts as the principal drivers of re-emergence, based on established understanding of highly transmissible childhood diseases with frequent epidemics. We extend an analytical approach to determine the number of 'skip' years preceding re-emergence for diseases with continuous seasonal transmission, population growth and under-reporting. Re-emergence times are shown to be highly sensitive to small changes in low R0 (secondary cases produced from a primary infection in a fully susceptible population). We then fit a stochastic Susceptible-Infected-Recovered (SIR) model to observed case data for the emergence of dengue serotype DENV1 in Rio de Janeiro. This aggregated city-level model substantially over-estimates observed re-emergence times either in terms of skips or outbreak probability under forward simulation. The inability of susceptible depletion and replenishment to explain re-emergence under 'well-mixed' conditions at a city-wide scale demonstrates a key limitation of SIR aggregated models, including those applied to other arboviruses. The predictive uncertainty and high skip sensitivity to epidemiological parameters suggest a need to investigate the relevant spatial scales of susceptible depletion and the scaling of microscale transmission dynamics to formulate simpler models that apply at coarse resolutions.

Mosquito-borne transmission in urban landscapes: the missing link between vector abundance and human density.

With escalating urbanization, the environmental, demographic, and socio-economic heterogeneity of urban landscapes poses a challenge to mathematical models for the transmission of vector-borne infections. Classical coupled vector-human models typically assume that mosquito abundance is either independent from, or proportional to, human population density, implying a decreasing force of infection, or per capita infection rate with host number. We question these assumptions by introducing an explicit dependence between host and vector densities through different recruitment functions, whose dynamical consequences we examine in a modified model formulation. Contrasting patterns in the force of infection are demonstrated, including in particular increasing trends when recruitment grows sufficiently fast with human density. Interaction of these patterns with seasonality in temperature can give rise to pronounced differences in timing, relative peak sizes, and duration of epidemics. These proposed dependencies explain empirical dengue risk patterns observed in the city of Delhi where socio-economic status has an impact on both human and mosquito densities. These observed risk trends with host density are inconsistent with current standard models. A better understanding of the connection between vector recruitment and host density is needed to address the population dynamics of mosquito-transmitted infections in urban landscapes.

Effects of scarcity and excess of larval food on life history traits of Aedes aegypti (Diptera: Culicidae).

Few studies have assessed the effects of food scarcity or excess on the life history traits of Aedes aegypti (L.) (Diptera: Culicidae) independently from larval density. We assessed immature survival, development time, and adult size in relation to food availability. We reared cohorts of 30 Ae. aegypti larvae from newly hatched to adult emergence with different food availability. Food conditions were kept constant by transferring larvae each day to a new food solution. Immature development was completed by some individuals in all treatments. The shortest development time, the largest adults, and the highest survival were observed at intermediate food levels. The most important effects of food scarcity were an extension in development time, a decrease in the size of adults, and a slight decrease in survival, while the most important effects of food excess were an important decrease in survival and a slight decrease in the size of adults. The variability in development time and adult size within sex and treatment increased at decreasing food availability. The results suggest that although the studied population has adapted to a wide range of food availabilities, both scarcity and excess of food have important negative impacts on fitness.

A model for the development of Aedes (Stegomyia) aegypti as a function of the available food.

We discuss the preimaginal development of the mosquito Aedes aegypti from the point of view of the statistics of developmental times and the final body-size of the pupae and adults. We begin the discussion studying existing models in relation to published data for the mosquito. The data suggest a developmental process that is described by exponentially distributed random times. The existing data show as well that the idea of cohorts emerging synchronously is verified only in optimal situations created at the laboratory but it is not verified in field experiments. We propose a model in which immature individuals progress in successive stages, all of them with exponentially distributed times, according to two different rates (one food-dependent and the other food-independent). This phenomenological model, coupled with a general model for growing, can explain the existing observations and new results produced in this work. The emerging picture is that the development of the larvae proceeds through a sequence of steps. Some of the steps depend on the available food. While food is in abundance, all steps can be thought as having equal duration, but when food is scarce, those steps that depend on food take considerably longer times. For insufficient levels of food, increase in larval mortality sets in. As a consequence of the smaller rates, the average pupation time increases and the cohort disperses in time. Dispersion, as measured by standard deviation, becomes a quadratic function of the average time indicating that cohort dispersion responds to the same causes than delays in pupation and adult emergence. During the whole developmental process the larva grows monotonically, initially at an exponential rate but later at decreasing rates, approaching a final body-size. Growth is stopped by maturation when it is already slow. As a consequence of this process, there is a slight bias favoring small individuals: Small individuals are born before larger individuals, although the tendency is very weak.