Meteorologically Driven Simulations of Dengue Epidemics in San Juan, PR

<div><p>Meteorological factors influence dengue virus ecology by modulating vector mosquito population dynamics, viral replication, and transmission. Dynamic modeling techniques can be used to examine how interactions among meteorological variables, vectors and the dengue virus influence transmission. We developed a dengue fever simulation model by coupling a dynamic simulation model for <i>Aedes aegypti</i>, the primary mosquito vector for dengue, with a basic epidemiological Susceptible-Exposed-Infectious-Recovered (SEIR) model. Employing a Monte Carlo approach, we simulated dengue transmission during the period of 2010–2013 in San Juan, PR, where dengue fever is endemic. The results of 9600 simulations using varied model parameters were evaluated by statistical comparison (r²) with surveillance data of dengue cases reported to the Centers for Disease Control and Prevention. To identify the most influential parameters associated with dengue virus transmission for each period the top 1% of best-fit model simulations were retained and compared. Using the top simulations, dengue cases were simulated well for 2010 (r² = 0.90, p = 0.03), 2011 (r² = 0.83, p = 0.05), and 2012 (r² = 0.94, p = 0.01); however, simulations were weaker for 2013 (r² = 0.25, p = 0.25) and the entire four-year period (r² = 0.44, p = 0.002). Analysis of parameter values from retained simulations revealed that rain dependent container habitats were more prevalent in best-fitting simulations during the wetter 2010 and 2011 years, while human managed (i.e. manually filled) container habitats were more prevalent in best-fitting simulations during the drier 2012 and 2013 years. The simulations further indicate that rainfall strongly modulates the timing of dengue (e.g., epidemics occurred earlier during rainy years) while temperature modulates the annual number of dengue fever cases. Our results suggest that meteorological factors have a time-variable influence on dengue transmission relative to other important environmental and human factors.</p></div

Simulated and reported weekly total dengue fever cases with model parameterized for individual years.

<p>The red line is reported DF cases, the black line is the ensemble mean of simulated DF cases, and the dashed gray lines are the ensemble maximum and minimum. Intra-annual variability is captured exceptionally well when the model is parameterized for the individual years 2010(A), 2011(B), and 2012(C). 2013(D) is not simulated as well.</p

Weekly summarized climate and simulated DF cases over San Juan, 2010–2103.

<p>The green bars are weekly total precipitation, the red line is weekly mean maximum temperature, the blue line is weekly mean minimum temperature, and the black line is weekly total simulated DF cases.</p

Monthly total precipitation (top) and mean temperature (bottom) by year.

<p>The highest incidence of DF cases occurred in 2010 and 2012 during which temperatures were highest in spring during 2010 and fall and early winter during 2012. The year 2011 had both the lowest DF incidence and the lowest temperatures. The year 2013 had moderate precipitation and temperatures but low DF incidence.</p

An Analysis of an Incomplete Marked Point Pattern of Heat-Related 911 Calls

<div><p>We analyze an incomplete marked point pattern of heat-related 911 calls between the years 2006–2010 in Houston, TX, to primarily investigate conditions that are associated with increased vulnerability to heat-related morbidity and, secondarily, build a statistical model that can be used as a public health tool to predict the volume of 911 calls given a time frame and heat exposure. We model the calls as arising from a nonhomogenous Cox process with unknown intensity measure. By using the kernel convolution construction of a Gaussian process, the intensity surface is modeled using a low-dimensional representation and properly adheres to circular domain constraints. We account for the incomplete observations by marginalizing the joint intensity measure over the domain of the missing marks and also demonstrate model based imputation. We find that spatial regions of high risk for heat-related 911 calls are temporally dynamic with the highest risk occurring in urban areas during the day. We also find that elderly populations have an increased probability of calling 911 with heat-related issues than younger populations. Finally, the age of individuals and hour of the day with the highest intensity of heat-related 911 calls varies by race/ethnicity. Supplementary materials are included with this article.</p></div

Ensemble model validation statistics for the top 1% of simulations for San Juan County, PR parameterized for the entire time period (2010–2013) and individual years.

<p>Bold indicates significance at p<0.05 and * indicates significance at p<0.01. O = observed, P = predicted, <i>S</i> = standard deviation, a = slope, b = intercept, MAE = mean average error, RMSE = route-mean-square error, s = systematic, u = unsystematic, d = Willmott’s index of agreement.</p><p>Ensemble model validation statistics for the top 1% of simulations for San Juan County, PR parameterized for the entire time period (2010–2013) and individual years.</p

Simulated and reported weekly total dengue fever cases (2010–2013).

<p>The model ensemble mean (black line) replicated inter-annual variability in reported DF cases (red line) accurately, however, intra-annual variability is not simulated as well. Dashed gray lines are the ensemble minimum and maximum.</p

Conceptual diagram of modeled dengue ecology.

<p>Suns bordering an arrow indicate that the process is temperature dependent and a habitat/container symbol bordering an arrow indicates the process is habitat/precipitation dependent. Water is added to a habitat through precipitation or manual filling and is lost due to spilling and evaporation which is regulated by temperature. After hatching, the mosquitoes develop through their larval and pupal stages before emerging as adults. The adults blood feed, develop eggs, and then lay them in a water habitat. Upon blood feeding, adults can contract the virus from an infectious human. Those mosquitoes can then expose a susceptible human to the virus during a subsequent blood meal.</p

Distribution of parameter values used in the top 1% of simulation for the entire time period (2010–2013) and individual years.

<p>The number of times each parameter value is used in the retained simulations is compared between years.</p

Average ranking of simulations using each parameter value when compared with reported case data for all years and individual years.

<p>Rankings were based on r² values with lower value ranks being better and higher value ranks being worse.</p