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Additional file 1. Includes additional methods, figures, and tables that complement the analysis and results presented in the main manuscript.

Additional file 1: A simple periodically forced difference equation model for mosquito population dynamics.

Additional file 8: S7. The Stan code for the delayed mortality model for both the intervention and the control arm in order to estimate the killing effect after biting.

Additional file 1. Supplementary data description and analysis.

Additional file 8: S7. The Stan code for the delayed mortality model for both the intervention and the control arm in order to estimate the killing effect after biting.

Additional file 3: Flowchart from operational to effective larviciding coverage.

Additional file 4: Re-simulated larviciding study in Mbita, western Kenya between 2002 and 2006.

Gambiense human African trypanosomiasis (gHAT) is a parasitic, vector-borne neglected tropical disease that has historically affected populations across West and Central Africa and can result in death if untreated. Following from the success of recent intervention programmes against gHAT, the World Health Organization (WHO) has defined a 2030 goal of global elimination of transmission (EOT). The key proposed indicator to measure achievement of the goal is to have zero reported cases. Results of previous mathematical modelling and quantitative analyses are brought together to explore both the implications of the proposed indicator and the feasibility of achieving the WHO goal. Whilst the indicator of zero case reporting is clear and measurable, it is an imperfect proxy for EOT and could arise either before or after EOT is achieved. Lagging reporting of infection and imperfect diagnostic specificity could result in case reporting after EOT, whereas the converse could be true due to underreporting, lack of coverage, and cryptic human and animal reservoirs. At the village-scale, the WHO recommendation of continuing active screening until there are three years of zero cases yields a high probability of local EOT, but extrapolating this result to larger spatial scales is complex. Predictive modelling of gHAT has consistently found that EOT by 2030 is unlikely across key endemic regions if current medical-only strategies are not bolstered by improved coverage, reduced time to detection and/or complementary vector control. Unfortunately, projected costs for strategies expected to meet EOT are high in the short term and strategies that are cost-effective in reducing burden are unlikely to result in EOT by 2030. Future modelling work should aim to provide predictions while taking into account uncertainties in stochastic dynamics and infection reservoirs, as well as assessment of multiple spatial scales, reactive strategies, and measurable proxies of EOT.

Highlights • Malaria and many other diseases are currently targeted for elimination. • Prioritisation of areas for elimination often occurs in an ad hoc manner. • In low transmission areas, prioritising higher transmission site reduces total burden. • In low transmission areas, prioritising higher transmission site reduces total costs. • In high transmission areas, prioritisation requires more detailed analysis., Malaria and some other tropical diseases are currently targeted for elimination and eventually eradication. Since resources are limited, prioritisation of countries or areas for elimination is often necessary. However, this prioritisation is frequently conducted in an ad hoc manner. Lower transmission areas are usually targeted for elimination first, but for some areas this necessitates long and potentially expensive surveillance programs while transmission is eliminated from neighbouring higher transmission areas. We use a mathematical model to compare the implications of prioritisation choices in reducing overall burden and costs. We show that when the duration of the elimination program is independent of the transmission potential, burden is always reduced most by targeting high transmission areas first, but to reduce costs the optimal ordering depends on the actual transmission levels. In general, when overall transmission potential is low and the surveillance cost per secondary case compared to the cost per imported case is low, targeting the higher transmission area for elimination first is favoured.

The trematode liver fluke, Opisthorchis viverrini, is endemic in Thailand, Lao People's Democratic Republic (Lao PDR) and Cambodia. Its life cycle involves humans, dogs and cats as definitive hosts; and snails and fish as intermediate hosts. Humans get infected through the consumption of raw or undercooked fish. A severe infection of O. viverrini can lead to cholangiocarcinoma, a mostly fatal bile duct cancer. Control activities include treatment of humans and domestic pets, health education on food consumption and improved sanitation. Mathematical modelling can help us to understand this multi-host disease system, identify weak points in the transmission cycle and determine the effectiveness of combinations of interventions to provide rational advice for the planning of control activities. Analysis and simulation of a series of mathematical models, ranging from deterministic ordinary differential equations models to stochastic individual-based models, calibrated to data from two islands in the Mekong river in Lao PDR, suggest that (i) mass drug administration is necessary for elimination of O. viverrini to be achieved as quickly as possible; (ii) sustainable education campaigns are as important as mass drug administration; and (iii) it is unlikely that cats and dogs are necessary for transmission to persist.

Background: Tuberculosis (TB) is the leading cause of death in the world from a single infectious disease and resulted in about 1.5 million deaths in 2018. Globally, close to one third of the population is latently infected with Mycobacterium tuberculosis (Mtb), of which about 5-10% during their lifetime, will develop active TB depending on the age of infection. TB is an airborne disease transmitted by airways from one person to the next when an uninfected individual breathes in air containing Mtb expired from an infected person. One way to estimate the risk of TB transmission begins with measuring environmental levels of carbon dioxide (CO2) levels exhaled by humans. CO2 levels combined with social contact data allow calculation of the volumes of rebreathed air in order to estimate the potential for airborne disease transmission by considering time at risk, quanta of contagion and the number of people occupying the confined space etc. Unfortunately, most TB patients are diagnosed at later stages of the disease, due to poor health-seeking behavior, inappropriate diagnostic investigations requested by health care providers, and limited access to better TB diagnostics which forces patients to seek relief by using self-prescribed medication. Delaying diagnosis and treatment of TB has important consequences for disease control at both the individual (poor treatment outcome) as well as the community level (continued transmission). Within the country, the prevalence of TB varies considerably across regions, and is higher among males, older persons, and those with lower socioeconomic status. Lastly, patients with TB often have additional comorbidities such as anemia, helminthiasis etc., which can result in poor treatment outcomes. Anemia of chronic disease is primarily found in patients with chronic disease status such as those with chronic immune activation such as TB and HIV-positive patients. Objectives: The overall goal of this PhD project was to understand the epidemiology and transmission of tuberculosis in Tanzania by studying the infrastructure-related risk of TB transmission through measuring environmental CO2 levels at locations of public importance; determining the TB diagnostic delay and its associated factors among TB patients; understanding differences in the epidemiology of TB and comorbidities among TB patients from rural and urban Tanzania; and investigating the association of hepcidin levels with coinfections, TB disease severity and progression from TB infection to disease among adults in Tanzania. Methods: This PhD project was embedded in the ongoing hospital-based cohort of adult TB cases and controls in Temeke, Dar es Salaam and Ifakara, Morogoro, Tanzania. The field work for this PhD study was carried out between September 2016 and April 2018. The project had two designs; infrastructure-related and patient-related design. For objective 1, an observational study was carried out which employed exposure assessment methods where we collected environmental data (CO2, geo-coordinates etc.,) from locations of public importance. We used the modified Wells-Riley equation to estimate the annual risk of TB transmission which was calculated as a function of time spent per year at a given location. For objectives 2-3, we analysed data from the ongoing cohort of adult TB patients and their household contacts (TB DAR). Briefly, recruitment of study participants began in 2013 at Temeke Regional Referral Hospital (Temeke district) and 2014 at the St. Francis Referral Hospital (Kilombero district). Participants collected baseline information and provided specimen for further analysis. Multivariate logistic regression models were used to find associations and presented as crude and adjusted odds ratio. Lastly, for objective 4, we designed a nested case-control study matched by age and sex. Descriptive statistics were used to summarize participants characteristics. We compared cases and controls using conditional logistic regression model to determine associations between outcomes of interest and various predictors. Results: We found that the annual risks of TB transmission were highest among prison inmates (41.6%) and drivers (20.3%) in public transport. Lower transmission risks were found in central markets (4.8% for traders, but 0.5% for their customers), passengers on public transport (2.4%), public schools (4.0%), nightclubs (1.7%), religious (0.13%), and social halls (0.12%). Diagnostic delay was positively associated with absence of chest pain (aOR = 7.97, 95% confidence intervals [CI] = 3.15 – 20.19), and presence of hemoptysis (aOR = 25.37, 95% CI = 11.15 – 57.74) and negatively associated with use of medication prior to TB diagnosis (aOR = 0.31, 95% CI = 0.14 – 0.71). Patients living far from pharmacies were less likely to visit a health care facility (incremental increase of distance versus visit to any facility: OR = 0.51, 95% CI = 0.28 – 0.96). Patients from the rural setting were older (median age 37 years vs. 34 years, p=0.003), had a lower median body mass index (17.5 kg/m2 vs. 18.5 kg/m2, p