Accelerating Progress Towards the 2030 Neglected Tropical Diseases Targets: How Can Quantitative Modeling Support Programmatic Decisions?

Over the past decade, considerable progress has been made in the control, elimination, and eradication of neglected tropical diseases (NTDs). Despite these advances, most NTD programs have recently experienced important setbacks; for example, NTD interventions were some of the most frequently and severely impacted by service disruptions due to the coronavirus disease 2019 (COVID-19) pandemic. Mathematical modeling can help inform selection of interventions to meet the targets set out in the NTD road map 2021-2030, and such studies should prioritize questions that are relevant for decision-makers, especially those designing, implementing, and evaluating national and subnational programs. In September 2022, the World Health Organization hosted a stakeholder meeting to identify such priority modeling questions across a range of NTDs and to consider how modeling could inform local decision making. Here, we summarize the outputs of the meeting, highlight common themes in the questions being asked, and discuss how quantitative modeling can support programmatic decisions that may accelerate progress towards the 2030 targets.

Evaluating elimination thresholds and stopping criteria for interventions against the vector-borne macroparasitic disease, lymphatic filariasis, using mathematical modelling.

We leveraged the ability of EPIFIL transmission models fit to field data to evaluate the use of the WHO Transmission Assessment Survey (TAS) for supporting Lymphatic Filariasis (LF) intervention stopping decisions. Our results indicate that understanding the underlying parasite extinction dynamics, particularly the protracted transient dynamics involved in shifts to the extinct state, is crucial for understanding the impacts of using TAS for determining the achievement of LF elimination. These findings warn that employing stopping criteria set for operational purposes, as employed in the TAS strategy, without a full consideration of the dynamics of extinction could seriously undermine the goal of achieving global LF elimination.

Combining predictive models with future change scenarios can produce credible forecasts of COVID-19 futures.

The advent and distribution of vaccines against SARS-CoV-2 in late 2020 was thought to represent an effective means to control the ongoing COVID-19 pandemic. This optimistic expectation was dashed by the omicron waves that emerged over the winter of 2021/2020 even in countries that had managed to vaccinate a large fraction of their populations, raising questions about whether it is possible to use scientific knowledge along with predictive models to anticipate changes and design management measures for the pandemic. Here, we used an extended SEIR model for SARS-CoV-2 transmission sequentially calibrated to data on cases and interventions implemented in Florida until Sept. 24th 2021, and coupled to scenarios of plausible changes in key drivers of viral transmission, to evaluate the capacity of such a tool for exploring the future of the pandemic in the state. We show that while the introduction of vaccinations could have led to the permanent, albeit drawn-out, ending of the pandemic if immunity acts over the long-term, additional futures marked by complicated repeat waves of infection become possible if this immunity wanes over time. We demonstrate that the most recent omicron wave could have been predicted by this hybrid system, but only if timely information on the timing of variant emergence and its epidemiological features were made available. Simulations for the introduction of a new variant exhibiting higher transmissibility than omicron indicated that while this will result in repeat waves, forecasted peaks are unlikely to reach that observed for the omicron wave owing to levels of immunity established over time in the population. These results highlight that while limitations of models calibrated to past data for precisely forecasting the futures of epidemics must be recognized, insightful predictions of pandemic futures are still possible if uncertainties about changes in key drivers are captured appropriately through plausible scenarios.

The relation between host competence and vector-feeding preference in a multi-host model: Chagas and Cutaneous Leishmaniasis.

Vector-borne diseases that occur in humans, as well as in domestic and wild reservoir hosts, cause a significant concern in public health, veterinary health, and ecological health in bio-diverse environments. The majority of vector-borne zoonotic diseases are transmitted among diverse host species, but different hosts have their own ability to transmit pathogens and to attract vectors. These combined transmission mechanisms in hosts and vectors are often called "host competencies" and "vector-feeding preferences." The purpose of this research is to assess the relationship between the host's ability to transmit the pathogen to vectors and the different feeding preferences for a specific host using a multi-host mathematical model. Working with zoonotic cutaneous leishmaniasis and Chagas disease, numerical simulations illustrate these vector-host populations' behavior together for the first time. Global sensitivity analyses confirm that the basic reproductive number, R0, is more sensitive to the the vector-demographic and biting-rate parameters in both diseases. Therefore, in this era of remarkable biodiversity loss and increased vector-borne diseases, it is crucial to understand how vector-host interaction mechanisms affect disease dynamics in humans within wildlife and domestic settings.

Complexity and critical thresholds in the dynamics of visceral leishmaniasis.

We study a general multi-host model of visceral leishmaniasis including both humans and animals, and where host and vector characteristics are captured via host competence along with vector biting preference. Additionally, the model accounts for spatial heterogeneity in human population and heterogeneity in biting behaviour of sandflies. We then use parameters for visceral leishmaniasis in the Indian subcontinent as an example and demonstrate that the model exhibits backward bifurcation, i.e. it has a human infection and a sandfly population threshold, characterized by a bi-stable region. These thresholds shift as a function of host competence, host population size, vector feeding preference, spatial heterogeneity, biting heterogeneity and control efforts. In particular, if control is applied through human treatment a new and lower human infection threshold is created, making elimination difficult to achieve, before eventually the human infection threshold no longer exists, making it impossible to control the disease by only reducing the infection levels below a certain threshold. A better strategy would be to reduce the human infection below a certain threshold potentially by early diagnosis, control animal population levels and keep the vector population under check. Spatial heterogeneity in human populations lowers the overall thresholds as a result of weak migration between patches.

Accelerating river blindness elimination by supplementing MDA with a vegetation "slash and clear" vector control strategy: a data-driven modeling analysis.

Attention is increasingly focusing on how best to accelerate progress toward meeting the WHO's 2030 goals for neglected tropical diseases (NTDs). For river blindness, a major NTD targeted for elimination, there is a long history of using vector control to suppress transmission, but traditional larvicide-based approaches are limited in their utility. One innovative and sustainable approach, "slash and clear", involves clearing vegetation from breeding areas, and recent field trials indicate that this technique very effectively reduces the biting density of Simulium damnosum s.s. In this study, we use a Bayesian data-driven mathematical modeling approach to investigate the potential impact of this intervention on human onchocerciasis infection. We developed a novel "slash and clear" model describing the effect of the intervention on seasonal black fly biting rates and coupled this with our population dynamics model of Onchocerca volvulus transmission. Our results indicate that supplementing annual drug treatments with "slash and clear" can significantly accelerate the achievement of onchocerciasis elimination. The efficacy of the intervention is not very sensitive to the timing of implementation, and the impact is meaningful even if vegetation is cleared only once per year. As such, this community-driven technique will represent an important option for achieving and sustaining O. volvulus elimination.

Effects of complexity and seasonality on backward bifurcation in vector-host models.

We study implications of complexity and seasonality in vector-host epidemiological models exhibiting backward bifurcation. Vector-host diseases represent complex infection systems that can vary in the transmission processes and population stages involved in disease progression. Seasonal fluctuations in external forcing factors can also interact in a complex way with internal host factors to govern the transmission dynamics. In backward bifurcation, the insufficiency of R0 < 1 for predicting the stability of the disease-free equilibrium (DFE) state arises due to existence of bistability (coexisting DFE and endemic equilibria) for a range of R0 values below one. Here we report that this region of bistability decreases with increasing complexity of vector-borne disease transmission as well as with increasing seasonality strength. The decreases in the bistability region are accompanied by a reduced force of infection acting on primary hosts. As a consequence, we show counterintuitively that a more complex vector-borne disease may be easier to control in settings of high seasonality.

Effects of quasiperiodic forcing in epidemic models.

We study changes in the bifurcations of seasonally driven compartmental epidemic models, where the transmission rate is modulated temporally. In the presence of periodic modulation of the transmission rate, the dynamics varies from periodic to chaotic. The route to chaos is typically through period doubling bifurcation. There are coexisting attractors for some sets of parameters. However in the presence of quasiperiodic modulation, tori are created in place of periodic orbits and chaos appears via finite torus doublings. Strange nonchaotic attractors (SNAs) are created at the boundary of chaotic and torus dynamics. Multistability is found to be reduced as a function of quasiperiodic modulation strength. It is argued that occurrence of SNAs gives an opportunity of asymptotic predictability of epidemic growth even when the underlying dynamics is strange.

Synchronization properties of coupled chaotic neurons: The role of random shared input.

Spike-time correlations of neighbouring neurons depend on their intrinsic firing properties as well as on the inputs they share. Studies have shown that periodically firing neurons, when subjected to random shared input, exhibit asynchronicity. Here, we study the effect of random shared input on the synchronization of weakly coupled chaotic neurons. The cases of so-called electrical and chemical coupling are both considered, and we observe a wide range of synchronization behaviour. When subjected to identical shared random input, there is a decrease in the threshold coupling strength needed for chaotic neurons to synchronize in-phase. The system also supports lag-synchronous states, and for these, we find that shared input can cause desynchronization. We carry out a master stability function analysis for a network of such neurons and show agreement with the numerical simulations. The contrasting role of shared random input for complete and lag synchronized neurons is useful in understanding spike-time correlations observed in many areas of the brain.

Synchronization and amplitude death in hypernetworks.

We study dynamical systems on a hypernetwork, namely by coupling them through several variables. For the case when the coupling(s) are all linear, a comprehensive analysis of the master stability function (MSF) for synchronized dynamics is presented and, through application to a number of paradigmatic examples, the typical forms of the MSF are discussed. The MSF formalism for hypernetworks also provides a framework to study synchronization in systems that are diffusively coupled through dissimilar variables-the so-called conjugate coupling that can lead to amplitude or oscillation death.