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System Dynamics (SD) models have been used to understand complex, multi-faceted dengue transmission dynamics, but a gap persists between research and actionable public health tools for decision-making. Spain is an at-risk country of imported dengue outbreaks, but only qualitative assessments are available to guide public health action and control. We propose a modular SD model combining temperature-dependent vector population, transmission parameters, and epidemiological interactions to simulate outbreaks from imported cases accounting for heterogeneous local climate-related transmission patterns. Under our assumptions, 15 provinces sustain vector populations capable of generating outbreaks from imported cases, with heterogeneous risk profiles regarding seasonality, magnitude and risk window shifting from late Spring to early Autum. Results being relative to given vector-to-human populations allow flexibility when translating outcomes between geographic scales. The model and the framework are meant to serve public health by incorporating transmission dynamics and quantitative-qualitative input to the evidence-based decision-making chain. It is a flexible tool that can easily adapt to changing contexts, parametrizations and epidemiological settings thanks to the modular approach.
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Dengue has broadened its global distribution substantially in the past two decades, and many endemic areas are experiencing increases in incidence. The Dominican Republic recently experienced its two largest outbreaks to date with 16,836 reported cases in 2015 and 20,123 reported cases in 2019. With continued increases in dengue transmission, developing tools to better prepare healthcare systems and mosquito control agencies is of critical importance. Before such tools can be developed, however, we must first better understand potential drivers of dengue transmission. To that end, we focus in this paper on determining relationships between climate variables and dengue transmission with an emphasis on eight provinces and the capital city of the Dominican Republic in the period 2015-2019. We present summary statistics for dengue cases, temperature, precipitation, and relative humidity in this period, and we conduct an analysis of correlated lags between climate variables and dengue cases as well as correlated lags among dengue cases in each of the nine locations. We find that the southwestern province of Barahona had the largest dengue incidence in both 2015 and 2019. Among all climate variables considered, lags between relative humidity variables and dengue cases were the most frequently correlated. We found that most locations had significant correlations with cases in other locations at lags of zero weeks. These results can be used to improve predictive models of dengue transmission in the country.
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System dynamics is a powerful tool that allows modeling of complex and highly networked systems such as those found in the human immune system. We have developed a model that reproduces how the exposure of human monocytes to lipopolysaccharides (LPSs) induces an inflammatory state characterized by high production of tumor necrosis factor alpha (TNFα), which is rapidly modulated to enter into a tolerant state, known as endotoxin tolerance (ET). The model contains two subsystems with a total of six states, seven flows, two auxiliary variables, and 14 parameters that interact through six differential and nine algebraic equations. The parameters were estimated and optimized to obtain a model that fits the experimental data obtained from human monocytes treated with various LPS doses. In contrast to publications on other animal models, stimulation of human monocytes with super-low-dose LPSs did not alter the response to a second LPSs challenge, neither inducing ET, nor enhancing the inflammatory response. Moreover, the model confirms the low production of TNFα and increased levels of C-C motif ligand 2 when monocytes exhibit a tolerant state similar to that of patients with sepsis. At present, the model can help us better understand the ET response and might offer new insights on sepsis diagnostics and prognosis by examining the monocyte response to endotoxins in patients with sepsis.
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BACKGROUND: This study examined changes in alcohol drinking patterns (DP) and associated variables in a Mediterranean country. METHODS: Changes in DP between baseline (2008-2010) and follow-up (2012-2013) were examined on a Spanish population-based cohort of 2254 adults (18-59 years) using multinomial logistic regression. Heavy consumption was defined as ≥40 g/day of alcohol in men (≥24 g/day in women) and binge drinking (BD) as the intake of ≥80 g of alcohol in men (≥60 g in women) on one occasion in the previous month. Six patterns were defined: (1) non-drinkers; (2) ex-drinkers; (3) moderate drinkers without BD (MNB); (4) moderate drinkers with BD (MB); (5) heavy drinkers without BD (HNB); and (6) heavy drinkers with BD (HB). RESULTS: Overall, 45.2% of participants changed DP during follow-up. Over 24% of non-drinkers and 19.4% of ex-drinkers at baseline qualified as MNB at follow-up. The largest flow was from HNB to MNB (57.1%). Light-drinking patterns experienced the largest gains (ex-drinkers: 37.5% and MNB: 66.7%) by absorbing individuals lost by heavy-drinking patterns (MB: 50.8% and HNB: 48.4%). Men, younger individuals, and current smokers were more likely to report heavy-drinking patterns at one or both assessments. Being married or employed increased the likelihood of reporting light-drinking patterns at both surveys (p<0.05). Improving physical quality of life and exercise were associated with a shift from light- to heavy-drinking pattern during follow-up (p<0.05). CONCLUSIONS: DP in Spain changed over 3 years with a tendency to "regress" toward moderate patterns. Repeated measures of alcohol intake may reduce classification errors and biased results when examining the impact of alcohol on health.