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Background: This paper presents a new agent-based model (ABM) for investigating T. b. rhodesiense human African trypanosomiasis (rHAT) disease dynamics, produced to aid a greater understanding of disease transmission, and essential for development of appropriate mitigation strategies. Methods: The ABM was developed to model rHAT incidence at a fine spatial scale along a 75 km transect in the Luangwa Valley, Zambia. The method offers a complementary approach to traditional compartmentalised modelling techniques, permitting incorporation of fine scale demographic data such as ethnicity, age and gender into the simulation. Results: Through identification of possible spatial, demographic and behavioural characteristics which may have differing implications for rHAT risk in the region, the ABM produced output that could not be readily generated by other techniques. On average there were 1.99 (S.E. 0.245) human infections and 1.83 (S.E. 0.183) cattle infections per 6 month period. The model output identified that the approximate incidence rate (per 1000 person-years) was lower amongst cattle owning households (0.079, S.E. 0.017), than those without cattle (0.134, S.E. 0.017). Immigrant tribes (e.g. Bemba I.R. = 0.353, S.E.0.155) and school-age children (e.g. 5–10 year old I.R. = 0.239, S.E. 0.041) were the most at-risk for acquiring infection. These findings have the potential to aid the targeting of future mitigation strategies. Conclusion: ABMs provide an alternative way of thinking about HAT and NTDs more generally, offering a solution to the investigation of local-scale questions, and which generate results that can be easily disseminated to those affected. The ABM can be used as a tool for scenario testing at an appropriate spatial scale to allow the design of logistically feasible mitigation strategies suggested by model output. This is of particular importance where resources are limited and management strategies are often pushed to the local scale. [ABSTRACT FROM AUTHOR]

In this paper we report a quantitative laser Biospeckle method using VDRL plates to monitor the activity of Trypanosoma cruzi and the calibration conditions including three image processing algorithms and three programs (ImageJ and two programs designed in this work). Benznidazole was used as a test drug. Variable volume (constant density) and variable density (constant volume) were used for the quantitative evaluation of parasite activity in calibrated wells of the VDRL plate. The desiccation process within the well was monitored as a function of volume and of the activity of the Biospeckle pattern of the parasites as well as the quantitative effect of the surface parasite quantity (proportion of the object’s plane). A statistical analysis was performed with ANOVA, Tukey post hoc and Descriptive Statistics using R and R Commander. Conditions of volume (100μl) and parasite density (2-4x104 parasites/well, in exponential growth phase), assay time (up to 204min), frame number (11 frames), algorithm and program (RCommander/SAGA) for image processing were selected to test the effect of variable concentrations of benznidazole (0.0195 to 20μg/mL / 0.075 to 76.8μM) at various times (1, 61, 128 and 204min) on the activity of the Biospeckle pattern. The flat wells of the VDRL plate were found to be suitable for the quantitative calibration of the activity of Trypanosoma cruzi using the appropriate algorithm and program. Under these conditions, benznidazole produces at 1min an instantaneous effect on the activity of the Biospeckle pattern of T. cruzi, which remains with a similar profile up to 1 hour. A second effect which is dependent on concentrations above 1.25μg/mL and is statistically different from the effect at lower concentrations causes a decrease in the activity of the Biospeckle pattern. This effect is better detected after 1 hour of drug action. This behavior may be explained by an instantaneous effect on a membrane protein of Trypanosoma cruzi that could mediate the translocation of benznidazole. At longer times the effect may possibly be explained by the required transformation of the pro-drug into the active drug. [ABSTRACT FROM AUTHOR]

Background: As of February 25, 2019, 875 cases of Ebola virus disease (EVD) were reported in North Kivu and Ituri Provinces, Democratic Republic of Congo. Since the beginning of October 2018, the outbreak has largely shifted into regions in which active armed conflict has occurred, and in which EVD cases and their contacts have been difficult for health workers to reach. We used available data on the current outbreak, with case-count time series from prior outbreaks, to project the short-term and long-term course of the outbreak. Methods: For short- and long-term projections, we modeled Ebola virus transmission using a stochastic branching process that assumes gradually quenching transmission rates estimated from past EVD outbreaks, with outbreak trajectories conditioned on agreement with the course of the current outbreak, and with multiple levels of vaccination coverage. We used two regression models to estimate similar projection periods. Short- and long-term projections were estimated using negative binomial autoregression and Theil-Sen regression, respectively. We also used Gott’s rule to estimate a baseline minimum-information projection. We then constructed an ensemble of forecasts to be compared and recorded for future evaluation against final outcomes. From August 20, 2018 to February 25, 2019, short-term model projections were validated against known case counts. Results: During validation of short-term projections, from one week to four weeks, we found models consistently scored higher on shorter-term forecasts. Based on case counts as of February 25, the stochastic model projected a median case count of 933 cases by February 18 (95% prediction interval: 872–1054) and 955 cases by March 4 (95% prediction interval: 874–1105), while the auto-regression model projects median case counts of 889 (95% prediction interval: 876–933) and 898 (95% prediction interval: 877–983) cases for those dates, respectively. Projected median final counts range from 953 to 1,749. Although the outbreak is already larger than all past Ebola outbreaks other than the 2013–2016 outbreak of over 26,000 cases, our models do not project that it is likely to grow to that scale. The stochastic model estimates that vaccination coverage in this outbreak is lower than reported in its trial setting in Sierra Leone. Conclusions: Our projections are concentrated in a range up to about 300 cases beyond those already reported. While a catastrophic outbreak is not projected, it is not ruled out, and prevention and vigilance are warranted. Prospective validation of our models in real time allowed us to generate more accurate short-term forecasts, and this process may prove useful for future real-time short-term forecasting. We estimate that transmission rates are higher than would be seen under target levels of 62% coverage due to contact tracing and vaccination, and this model estimate may offer a surrogate indicator for the outbreak response challenges. [ABSTRACT FROM AUTHOR]

Endemic to the Democratic Republic of the Congo (DRC), monkeypox is a zoonotic disease that causes smallpox-like illness in humans. Observed fluctuations in reported cases over time raises questions about when it is appropriate to mount a public health response, and what specific actions should be taken. We evaluated three different thresholds to differentiate between baseline and heightened disease incidence, and propose a novel, tiered algorithm for public health action. Monkeypox surveillance data from Tshuapa Province, 2011–2013, were used to calculate three different statistical thresholds: Cullen, c-sum, and a World Health Organization (WHO) method based on monthly incidence. When the observed cases exceeded the threshold for a given month, that month was considered to be ‘aberrant’. For each approach, the number of aberrant months detected was summed by year—each method produced vastly different results. The Cullen approach generated a number of aberrant signals over the period of consideration (9/36 months). The c-sum method was the most sensitive (30/36 months), followed by the WHO method (12/24 months). We conclude that triggering public health action based on signals detected by a single method may be inefficient and overly simplistic for monkeypox. We propose instead a response algorithm that integrates an objective threshold (WHO method) with contextual information about epidemiological and spatiotemporal links between suspected cases to determine whether a response should be operating under i) routine surveillance ii) alert status, or iii) outbreak status. This framework could be modified and adopted by national and zone level health workers in monkeypox-endemic countries. Lastly, we discuss considerations for selecting thresholds for monkeypox outbreaks across gradients of endemicity and public health resources. [ABSTRACT FROM AUTHOR]

Background: This study aimed to investigate the spatiotemporal clustering and socio-environmental factors associated with dengue fever (DF) incidence rates at street level in Guangzhou City, China. Methods: Spatiotemporal scan technique was applied to identify the high risk region of DF. Multiple regression model was used to identify the socio-environmental factors associated with DF infection. A Poisson regression model was employed to examine the spatiotemporal patterns in the spread of DF. Results: Spatial clusters of DF were primarily concentrated at the southwest part of Guangzhou City. Age group (65+ years) (Odd Ratio (OR) = 1.49, 95% Confidence Interval = 1.13 to 2.03), floating population (OR = 1.09, 95% CI = 1.05 to 1.15), low-education (OR = 1.08, 95% CI = 1.01 to 1.16) and non-agriculture (OR = 1.07, 95% CI = 1.03 to 1.11) were associated with DF transmission. Poisson regression results indicated that changes in DF incidence rates were significantly associated with longitude (β = -5.08, P<0.01) and latitude (β = -1.99, P<0.01). Conclusions: The study demonstrated that social-environmental factors may play an important role in DF transmission in Guangzhou. As geographic range of notified DF has significantly expanded over recent years, an early warning systems based on spatiotemporal model with socio-environmental is urgently needed to improve the effectiveness and efficiency of dengue control and prevention. [ABSTRACT FROM AUTHOR]

Background: During the past 20 years, enormous efforts have been expended globally to eliminate lymphatic filariasis (LF) through mass drug administration (MDA). However, small endemic foci (microfoci) of LF may threaten the presumed inevitable decline of infections after MDA cessation. We conducted microsimulation modeling to assess the ability of different types of surveillance to identify microfoci in these settings. Methods: Five or ten microfoci of radius 1, 2, or 3 km with infection marker prevalence (intensity) of 3, 6, or 10 times background prevalence were placed in spatial simulations, run in R Version 3.2. Diagnostic tests included microfilaremia, immunochromatographic test (ICT), and Wb123 ELISA. Population size was fixed at 360,000 in a 60 x 60 km area; demographics were based on literature for Sub-Saharan African populations. Background ICT prevalence in 6–7 year olds was anchored at 1.0%, and the prevalence in the remaining population was adjusted by age. Adults≥18 years, women aged 15–40 years (WCBA), children aged 6–7 years, or children≤5 years were sampled. Cluster (CS), simple random sampling (SRS), and TAS-like sampling were simulated, with follow-up testing of the nearest 20, 100, or 500 persons around each infection-marker-positive person. A threshold number of positive persons in follow-up testing indicated a suspected microfocus. Suspected microfoci identified during surveillance and actual microfoci in the simulation were compared to obtain a predictive value positive (PVP). Each parameter set was referred to as a protocol. Protocols were scored by efficiency, defined as the most microfoci identified, the fewest persons requiring primary and follow-up testing, and the highest PVP. Negative binomial regression was used to estimate aggregate effects of different variables on efficiency metrics. Results: All variables were significantly associated with efficiency metrics. Additional follow-up tests beyond 20 did not greatly increase the number of microfoci detected, but significantly negatively impacted efficiency. Of 3,402 protocols evaluated, 384 (11.3%) identified all five microfoci (PVP 3.4–100.0%) and required testing 0.73–35.6% of the population. All used SRS and 378 (98.4%) only identified all five microfoci if they were 2–3 km diameter or high-intensity (6x or 10x); 374 (97.4%) required ICT or Wb123 testing to identify all five microfoci, and 281 (73.0%) required sampling adults or WCBA. The most efficient CS protocols identified two (40%) microfoci. After limiting to protocols with 1-km radius microfoci of 3x intensity (n = 378), eight identified all five microfoci; all used SRS and ICT and required testing 31.2–33.3% of the population. The most efficient CS and TAS-like protocols as well as those using microfilaremia testing identified only one (20%) microfocus when they were limited to 1-km radius and 3x intensity. Conclusion: In this model, SRS, ICT, and sampling of adults maximized microfocus detection efficiency. Follow-up sampling of more persons did not necessarily increase protocol efficiency. Current approaches towards surveillance, including TAS, may not detect small, low-intensity LF microfoci that could remain after cessation of MDA. The model provides many surveillance protocols that can be selected for optimal outcomes. [ABSTRACT FROM AUTHOR]

Background: The 2014–6 West African Ebola epidemic highlights the need for rigorous, rapid clinical trial methods for vaccines. A challenge for trial design is making sample size calculations based on incidence within the trial, total vaccine effect, and intracluster correlation, when these parameters are uncertain in the presence of indirect effects of vaccination. Methods and findings: We present a stochastic, compartmental model for a ring vaccination trial. After identification of an index case, a ring of contacts is recruited and either vaccinated immediately or after 21 days. The primary outcome of the trial is total vaccine effect, counting cases only from a pre-specified window in which the immediate arm is assumed to be fully protected and the delayed arm is not protected. Simulation results are used to calculate necessary sample size and estimated vaccine effect. Under baseline assumptions about vaccine properties, monthly incidence in unvaccinated rings and trial design, a standard sample-size calculation neglecting dynamic effects estimated that 7,100 participants would be needed to achieve 80% power to detect a difference in attack rate between arms, while incorporating dynamic considerations in the model increased the estimate to 8,900. This approach replaces assumptions about parameters at the ring level with assumptions about disease dynamics and vaccine characteristics at the individual level, so within this framework we were able to describe the sensitivity of the trial power and estimated effect to various parameters. We found that both of these quantities are sensitive to properties of the vaccine, to setting-specific parameters over which investigators have little control, and to parameters that are determined by the study design. Conclusions: Incorporating simulation into the trial design process can improve robustness of sample size calculations. For this specific trial design, vaccine effectiveness depends on properties of the ring vaccination design and on the measurement window, as well as the epidemiologic setting. [ABSTRACT FROM AUTHOR]

Taenia solium taeniosis/cysticercosis was declared eradicable by the International Task Force for Disease Eradication in 1993, but remains a neglected zoonosis. To assist in the attempt to regionally eliminate this parasite, we developed cystiSim, an agent-based model for T. solium transmission and control. The model was developed in R and available as an R package (). cystiSim was adapted to an observed setting using field data from Tanzania, but adaptable to other settings if necessary. The model description adheres to the Overview, Design concepts, and Details (ODD) protocol and consists of two entities—pigs and humans. Pigs acquire cysticercosis through the environment or by direct contact with a tapeworm carrier's faeces. Humans acquire taeniosis from slaughtered pigs proportional to their infection intensity. The model allows for evaluation of three interventions measures or combinations hereof: treatment of humans, treatment of pigs, and pig vaccination, and allows for customary coverage and efficacy settings. cystiSim is the first agent-based transmission model for T. solium and suggests that control using a strategy consisting of an intervention only targeting the porcine host is possible, but that coverage and efficacy must be high if elimination is the ultimate goal. Good coverage of the intervention is important, but can be compensated for by including an additional intervention targeting the human host. cystiSim shows that the scenarios combining interventions in both hosts, mass drug administration to humans, and vaccination and treatment of pigs, have a high probability of success if coverage of 75% can be maintained over at least a four year period. In comparison with an existing mathematical model for T. solium transmission, cystiSim also includes parasite maturation, host immunity, and environmental contamination. Adding these biological parameters to the model resulted in new insights in the potential effect of intervention measures. [ABSTRACT FROM AUTHOR]

The epidemiology of dengue fever is characterized by highly seasonal, multi-annual fluctuations, and the irregular circulation of its four serotypes. It is believed that this behaviour arises from the interplay between environmental drivers and serotype interactions. The exact mechanism, however, is uncertain. Constraining mathematical models to patterns characteristic to dengue epidemiology offers a means for detecting such mechanisms. Here, we used a pattern-oriented modelling (POM) strategy to fit and assess a range of dengue models, driven by combinations of temporary cross protective-immunity, cross-enhancement, and seasonal forcing, on their ability to capture the main characteristics of dengue dynamics. We show that all proposed models reproduce the observed dengue patterns across some part of the parameter space. Which model best supports the dengue dynamics is determined by the level of seasonal forcing. Further, when tertiary and quaternary infections are allowed, the inclusion of temporary cross-immunity alone is strongly supported, but the addition of cross-enhancement markedly reduces the parameter range at which dengue dynamics are produced, irrespective of the strength of seasonal forcing. The implication of these structural uncertainties on predicted vulnerability to control is also discussed. With ever expanding spread of dengue, greater understanding of dengue dynamics and control efforts (e.g. a near-future vaccine introduction) has become critically important. This study highlights the capacity of multi-level pattern-matching modelling approaches to offer an analytic tool for deeper insights into dengue epidemiology and control. [ABSTRACT FROM AUTHOR]