The epidemiological impact of an HIV vaccine on the HIV/AIDS epidemic in Southern India

The potential epidemiological impact of preventive HIV vaccines on the HIV epidemic in Southern India is examined using a mathematical deterministic dynamic compartmental model. Various assumptions about the degree of protection offered by such a vaccine, the extent of immunological response of those vaccinated, and the duration of protection afforded are explored. Alternative targeting strategies for HIV vaccination are simulated and compared with the impact of conventional prevention interventions in high-risk groups and the general population. The impact of disinhibition (increased risk behavior due to the presence of a vaccine) is also considered. Vaccines that convey a high degree of protection in a share of or all of those immunized and that convey life-long immunity are the most effective in curbing the HIV epidemic. Vaccines that convey less than complete protection may also have substantial public health impact, but disinhibition can easily undo their effects and they should be used combined with conventional prevention efforts. Conventional interventions that target commercial sex workers and their clients to increase condom use can also be highly effective and can be implemented immediately, before the arrival of vaccines.Poverty and Health,Disease Control&Prevention,Health Monitoring&Evaluation,Public Health Promotion,HIV AIDS,HIV AIDS,Health Monitoring&Evaluation,Adolescent Health,HIV AIDS and Business,Health Service Management and Delivery

Spatial and temporal variations of malaria epidemic risk in Ethiopia: factors involved and implications.

The aim of this study was to describe spatial and temporal variations in malaria epidemic risk in Ethiopia and to examine factors involved in relation to their implications for early warning and interpretation of geographical risk models. Forty-eight epidemic episodes were identified in various areas between September 1986 and August 1993 and factors that might have led to the events investigated using health facility records and weather data. The study showed that epidemics in specific years were associated with specific geographical areas. A major epidemic in 1988 affected the highlands whereas epidemics in 1991 and 1992 affected highland-fringe areas on the escarpments of the Rift Valley and in southern and north-western parts of the country. Malaria epidemics were significantly more often preceded by a month of abnormally high minimum temperature in the preceding 3 months than based on random chance, whereas frequency of abnormally low minimum temperature prior to epidemics was significantly lower than expected. Abnormal increases of maximum temperature and rainfall had no positive association with the epidemics. A period of low incidence during previous transmission seasons might have aggravated the events, possibly due to low level of immunity in affected populations. Epidemic risk is a dynamic phenomenon with changing geographic pattern based on temporal variations in determinant factors including weather and other eco-epidemiological characteristics of areas at risk. Epidemic early warning systems should take account of non-uniform effects of these factors by space and time and thus temporal dimensions need to be considered in spatial models of epidemic risks

Advances and challenges in predicting the impact of lymphatic filariasis elimination programmes by mathematical modelling

Mathematical simulation models for transmission and control of lymphatic filariasis are useful tools for studying the prospects of lymphatic filariasis elimination. Two simulation models are currently being used. The first, EPIFIL, is a population-based, deterministic model that simulates average trends in infection intensity over time. The second, LYMFASIM, is an individual-based, stochastic model that simulates acquisition and loss of infection for each individual in the simulated population, taking account of individual characteristics. For settings like Pondicherry (India), where Wuchereria bancrofti infection is transmitted by Culex quinquefasciatus, the models give similar predictions of the coverage and number of treatment rounds required to bring microfilaraemia prevalence below a level of 0.5%. Nevertheless, published estimates of the duration of mass treatment required for elimination differed, due to the use of different indicators for elimination (EPIFIL: microfilaraemia prevalence < 0.5% after the last treatment; LYMFASIM: reduction of microfilaraemia prevalence to zero, within 40 years after the start of mass treatment). The two main challenges for future modelling work are: 1) quantification and validation of the models for other regions, for investigation of elimination prospects in situations with other vector-parasite combinations and endemicity levels than in Pondicherry; 2) application of the models to address a range of programmatic issues related to the monitoring and evaluation of ongoing control programmes. The models' usefulness could be enhanced by several extensions; inclusion of different diagnostic tests and natural history of disease in the models is of particular relevance

Forecasting malaria incidence from historical morbidity patterns in epidemic-prone areas of Ethiopia: a simple seasonal adjustment method performs best.

The aim of this study was to assess the accuracy of different methods of forecasting malaria incidence from historical morbidity patterns in areas with unstable transmission. We tested five methods using incidence data reported from health facilities in 20 areas in central and north-western Ethiopia. The accuracy of each method was determined by calculating errors resulting from the difference between observed incidence and corresponding forecasts obtained for prediction intervals of up to 12 months. Simple seasonal adjustment methods outperformed a statistically more advanced autoregressive integrated moving average method. In particular, a seasonal adjustment method that uses mean deviation of the last three observations from expected seasonal values consistently produced the best forecasts. Using 3 years' observation to generate forecasts with this method gave lower errors than shorter or longer periods. Incidence during the rainy months of June-August was the most predictable with this method. Forecasts for the normally dry months, particularly December-February, were less accurate. The study shows the limitations of forecasting incidence from historical morbidity patterns alone, and indicates the need for improved epidemic early warning by incorporating external predictors such as meteorological factors

Predicting the risk and speed of drug resistance emerging in soil-transmitted helminths during preventive chemotherapy

Control of soil-transmitted helminths relies heavily on regular large-scale deworming of high-risk groups (e.g., children) with benzimidazole derivatives. Although drug resistance has not yet been documented in human soil-transmitted helminths, regular deworming of cattle and sheep has led to widespread benzimidazole resistance in veterinary helminths. Here we predict the population dynamics of human soil-transmitted helminth infections and drug resistance during 20 years of regular preventive chemotherapy, using an individual-based model. With the current preventive chemotherapy strategy of mainly targeting children in schools, drug resistance may evolve in soil-transmitted helminths within a decade. More intense preventive chemotherapy strategies increase the prospects of soil-transmitted helminths elimination, but also increase the speed at which drug efficacy declines, especially when implementing community-based preventive chemotherapy (population-wide deworming). If during the last decade, preventive chemotherapy against soil-transmitted helminths has led to resistance, we may not have detected it as drug efficacy has not been structurally monitored, or incorrectly so. These findings highlight the need to develop and implement strategies to monitor and mitigate the evolution of benzimidazole resistance.</p

Predicting the risk and speed of drug resistance emerging in soil-transmitted helminths during preventive chemotherapy

Control of soil-transmitted helminths relies heavily on regular large-scale deworming of high-risk groups (e.g., children) with benzimidazole derivatives. Although drug resistance has not yet been documented in human soil-transmitted helminths, regular deworming of cattle and sheep has led to widespread benzimidazole resistance in veterinary helminths. Here we predict the population dynamics of human soil-transmitted helminth infections and drug resistance during 20 years of regular preventive chemotherapy, using an individual-based model. With the current preventive chemotherapy strategy of mainly targeting children in schools, drug resistance may evolve in soil-transmitted helminths within a decade. More intense preventive chemotherapy strategies increase the prospects of soil-transmitted helminths elimination, but also increase the speed at which drug efficacy declines, especially when implementing community-based preventive chemotherapy (population-wide deworming). If during the last decade, preventive chemotherapy against soil-transmitted helminths has led to resistance, we may not have detected it as drug efficacy has not been structurally monitored, or incorrectly so. These findings highlight the need to develop and implement strategies to monitor and mitigate the evolution of benzimidazole resistance.</p

How to optimize tuberculosis case finding: explorations for Indonesia with a health system model

BACKGROUND: A mathematical model was designed to explore the impact of three strategies for better tuberculosis case finding. Strategies included: (1) reducing the number of tuberculosis patients who do not seek care; (2) reducing diagnostic delay; and (3) engaging non-DOTS providers in the referral of tuberculosis suspects to DOTS services in the Indonesian health system context. The impact of these strategies on tuberculosis mortality and treatment outcome was estimated using a mathematical model of the Indonesian health system. METHODS: The model consists of multiple compartments representing logical movement of a respiratory symptomatic (tuberculosis suspect) through the health system, including patient- and health system delays. Main outputs of the model are tuberculosis death rate and treatment outcome (i.e. full or partial cure). We quantified the model parameters for the Jogjakarta province context, using a two round Delphi survey with five Indonesian tuberculosis experts. RESULTS: The model validation shows that four critical model outputs (average duration of symptom onset to treatment, detection rate, cure rate, and death rate) were reasonably close to existing available data, erring towards more optimistic outcomes than are actually reported. The model predicted that an intervention to reduce the proportion of tuberculosis patients who never seek care would have the biggest impact on tuberculosis death prevention, while an intervention resulting in more referrals of tuberculosis suspects to DOTS facilities would yield higher cure rates. This finding is similar for situations where the alternative sector is a more important health resource, such as in most other parts of Indonesia. CONCLUSION: We used mathematical modeling to explore the impact of Indonesian health system interventions on tuberculosis treatment outcome and deaths. Because detailed data were not available regarding the current Indonesian population, we relied on expert opinion to quantify the parameters. The fact that the model output showed similar results to epidemiological data suggests that the experts had an accurate understanding of this subject, thereby reassuring the quality of our predictions. The model highlighted the potential effectiveness of active case finding of tuberculosis patients with limited access to DOTS facilities in the developing country setting

Revisiting the impact of Schistosoma mansoni regulating mechanisms on transmission dynamics using SchiSTOP, a novel modelling framework

BACKGROUND: The transmission cycle of Schistosoma is remarkably complex, including sexual reproduction in human hosts and asexual reproduction in the intermediate host (freshwater snails). Patterns of rapid recrudescence after treatment and stable low transmission are often observed, hampering the achievement of control targets. Current mathematical models commonly assume regulation of transmission to occur at worm level through density-dependent egg production. However, conclusive evidence on this regulating mechanism is weak, especially for S. mansoni. In this study, we explore the interplay of different regulating mechanisms and their ability to explain observed patterns in S. mansoni epidemiology. METHODOLOGY/PRINCIPAL FINDINGS: We developed SchiSTOP: a hybrid stochastic agent-based and deterministic modelling framework for S. mansoni transmission in an age-structured human population. We implemented different models with regulating mechanisms at: i) worm-level (density-dependent egg production), ii) human-level (anti-reinfection immunity), and iii) snail-level (density-dependent snail dynamics). Additionally, we considered two functional choices for the age-specific relative exposure to infection. We assessed the ability of each model to reproduce observed epidemiological patterns pre- and post-control, and compared successful models in their predictions of the impact of school-based and community-wide treatment. Simulations confirmed that assuming at least one regulating mechanism is required to reproduce a stable endemic equilibrium. Snail-level regulation was necessary to explain stable low transmission, while models combining snail- and human-level regulation with an age-exposure function informed with water contact data were successful in reproducing a rapid rebound after treatment. However, the predicted probability of reaching the control targets varied largely across models. CONCLUSIONS/SIGNIFICANCE: The choice of regulating mechanisms in schistosomiasis modelling largely determines the expected impact of control interventions. Overall, this work suggests that reaching the control targets solely through mass drug administration may be more challenging than currently thought. We highlight the importance of regulating mechanisms to be included in transmission models used for policy.</p

Modelling the impact of COVID-19-related programme interruptions on visceral leishmaniasis in India

BACKGROUND: In March 2020, India declared a nationwide lockdown to control the spread of coronavirus disease 2019. As a result, control efforts against visceral leishmaniasis (VL) were interrupted. METHODS: Using an established age-structured deterministic VL transmission model, we predicted the impact of a 6- to 24-month programme interruption on the timeline towards achieving the VL elimination target as well as on the increase of VL cases. We also explored the potential impact of a mitigation strategy after the interruption. RESULTS: Delays towards the elimination target are estimated to range between 0 and 9 y. Highly endemic settings where control efforts have been ongoing for 5-8 y are most affected by an interruption, for which we identified a mitigation strategy to be most relevant. However, more importantly, all settings can expect an increase in the number of VL cases. This increase is substantial even for settings with a limited expected delay in achieving the elimination target. CONCLUSIONS: Besides implementing mitigation strategies, it is of great importance to try and keep the duration of the interruption as short as possible to prevent new individuals from becoming infected with VL and continue the efforts towards VL elimination as a public health problem in India