We estimate the relative transmission rate in early versus later infection among men who have sex with men (MSM) in San Francisco, California, by studying the characteristics of a sample of transmitters, recruited through newly diagnosed, recently infected MSM between 1996 and 2009. Of 36 transmitters identified, 9 were determined on the basis of testing history and serologic testing to have been recently infected. The unadjusted odds ratio of transmitting during early infection was 15.2 (95% confidence interval [CI], 6.3-33.4; P < .001); the odds ratio was 8.9 (95% CI, 4.1-19.4) after adjustment for self-reported antiretroviral treatment. This high transmissibility could be due to both high infectiousness and high rates of sex partner change or concurrent partnerships.

Recent mathematical and statistical modeling of leprosy incidence data provides estimates of the current undiagnosed population and projections of diagnosed cases, as well as ongoing transmission. Furthermore, modeling studies have been used to evaluate the effectiveness of proposed intervention strategies, such as postleprosy exposure prophylaxis and novel diagnostics, relative to current approaches. Such modeling studies have revealed both a slow decline of new cases and a substantial pool of undiagnosed infections. These findings highlight the need for active case detection, particularly targeting leprosy foci, as well as for continued research into innovative accurate, rapid, and cost-effective diagnostics. As leprosy incidence continues to decline, targeted active case detection primarily in foci and connected areas will likely become increasingly important.

Background

The COVID-19 pandemic has disrupted planned annual antibiotic mass drug administration (MDA) activities that have formed the cornerstone of the largely successful global efforts to eliminate trachoma as a public health problem.

Methods

Using a mathematical model we investigate the impact of interruption to MDA in trachoma-endemic settings. We evaluate potential measures to mitigate this impact and consider alternative strategies for accelerating progress in those areas where the trachoma elimination targets may not be achievable otherwise.

Results

We demonstrate that for districts that were hyperendemic at baseline, or where the trachoma elimination thresholds have not already been achieved after three rounds of MDA, the interruption to planned MDA could lead to a delay to reaching elimination targets greater than the duration of interruption. We also show that an additional round of MDA in the year following MDA resumption could effectively mitigate this delay. For districts where the probability of elimination under annual MDA was already very low, we demonstrate that more intensive MDA schedules are needed to achieve agreed targets.

Conclusion

Through appropriate use of additional MDA, the impact of COVID-19 in terms of delay to reaching trachoma elimination targets can be effectively mitigated. Additionally, more frequent MDA may accelerate progress towards 2030 goals.

Background

As malaria prevalence declines in many parts of the world due to widescale control efforts and as drug-resistant parasites begin to emerge, a quantitative understanding of human movement is becoming increasingly relevant to malaria control. However, despite its importance, significant knowledge gaps remain regarding human movement, particularly in sub-Saharan Africa.

Methods

A quantitative survey of human movement patterns was conducted in four countries in sub-Saharan Africa: Mali, Burkina Faso, Zambia, and Tanzania, with three to five survey locations chosen in each country. Questions were included on demographic and trip details, malaria risk behaviour, children accompanying travellers, and mobile phone usage to enable phone signal data to be better correlated with movement. A total of 4352 individuals were interviewed and 6411 trips recorded.

Results

A cluster analysis of trips highlighted two distinct traveller groups of relevance to malaria transmission: women travelling with children (in all four countries) and youth workers (in Mali). Women travelling with children were more likely to travel to areas of relatively high malaria prevalence in Mali (OR = 4.46, 95% CI = 3.42-5.83), Burkina Faso (OR = 1.58, 95% CI = 1.23-1.58), Zambia (OR = 1.50, 95% CI = 1.20-1.89), and Tanzania (OR = 2.28, 95% CI = 1.71-3.05) compared to other travellers. They were also more likely to own bed nets in Burkina Faso (OR = 1.77, 95% CI = 1.25-2.53) and Zambia (OR = 1.74, 95% CI = 1.34 2.27), and less likely to own a mobile phone in Mali (OR = 0.50, 95% CI = 0.39-0.65), Burkina Faso (OR = 0.39, 95% CI = 0.30-0.52), and Zambia (OR = 0.60, 95% CI = 0.47-0.76). Malian youth workers were more likely to travel to areas of relatively high malaria prevalence (OR = 23, 95% CI = 17-31) and for longer durations (mean of 70 days cf 21 days, p < 0.001) compared to other travellers.

Conclusions

Women travelling with children were a remarkably consistent traveller group across all four countries surveyed. They are expected to contribute greatly towards spatial malaria transmission because the children they travel with tend to have high parasite prevalence. Youth workers were a significant traveller group in Mali and are expected to contribute greatly to spatial malaria transmission because their movements correlate with seasonal rains and hence peak mosquito densities. Interventions aimed at interrupting spatial transmission of parasites should consider these traveller groups.

Locally tailored interventions for neglected tropical diseases (NTDs) are becoming increasingly important for ensuring that the World Health Organization (WHO) goals for control and elimination are reached. Mathematical models, such as those developed by the NTD Modelling Consortium, are able to offer recommendations on interventions but remain constrained by the data currently available. Data collection for NTDs needs to be strengthened as better data are required to indirectly inform transmission in an area. Addressing specific data needs will improve our modelling recommendations, enabling more accurate tailoring of interventions and assessment of their progress. In this collection, we discuss the data needs for several NTDs, specifically gambiense human African trypanosomiasis, lymphatic filariasis, onchocerciasis, schistosomiasis, soil-transmitted helminths (STH), trachoma, and visceral leishmaniasis. Similarities in the data needs for these NTDs highlight the potential for integration across these diseases and where possible, a wider spectrum of diseases.

Background

Tremendous progress towards elimination of trachoma as a public health problem has been made. However, there are areas where the clinical indicator of disease, trachomatous inflammation-follicular (TF), remains prevalent. We quantify the progress that has been made, and forecast how TF prevalence will evolve with current interventions. We also determine the probability that a district is a transmission-hotspot based on its TF prevalence (ie, reproduction number greater than one).

Methods

Data on trachoma prevalence come from the GET2020 global repository organized by the World Health Organization and the International Trachoma Initiative. Forecasts of TF prevalence and the percent of districts with local control is achieved by regressing the coefficients of a fitted exponential distribution for the year-by-year distribution of TF prevalence. The probability of a district being a transmission-hotspot is extrapolated from the residuals of the regression.

Results

Forecasts suggest that with current interventions, 96.5% of surveyed districts will have TF prevalence among children aged 1-9 years <5% by 2030 (95% CI: 86.6%-100.0%). Districts with TF prevalence < 20% appear unlikely to be transmission-hotspots. However, a district having TF prevalence of over 28% in 2016-2019 corresponds to at least 50% probability of being a transmission-hotspot.

Conclusions

Sustainable control of trachoma appears achievable. However there are transmission-hotspots that are not responding to annual mass drug administration of azithromycin and require enhanced treatment in order to reach local control.

Despite some notable successes in the control of infectious diseases, transmissible pathogens still pose an enormous threat to human and animal health. The ecological and evolutionary dynamics of infections play out on a wide range of interconnected temporal, organizational, and spatial scales, which span hours to months, cells to ecosystems, and local to global spread. Moreover, some pathogens are directly transmitted between individuals of a single species, whereas others circulate among multiple hosts, need arthropod vectors, or can survive in environmental reservoirs. Many factors, including increasing antimicrobial resistance, increased human connectivity and changeable human behavior, elevate prevention and control from matters of national policy to international challenge. In the face of this complexity, mathematical models offer valuable tools for synthesizing information to understand epidemiological patterns, and for developing quantitative evidence for decision-making in global health.

Due to the COVID-19 pandemic, many key neglected tropical disease (NTD) activities have been postponed. This hindrance comes at a time when the NTDs are progressing towards their ambitious goals for 2030. Mathematical modelling on several NTDs, namely gambiense sleeping sickness, lymphatic filariasis, onchocerciasis, schistosomiasis, soil-transmitted helminthiases (STH), trachoma, and visceral leishmaniasis, shows that the impact of this disruption will vary across the diseases. Programs face a risk of resurgence, which will be fastest in high-transmission areas. Furthermore, of the mass drug administration diseases, schistosomiasis, STH, and trachoma are likely to encounter faster resurgence. The case-finding diseases (gambiense sleeping sickness and visceral leishmaniasis) are likely to have fewer cases being detected but may face an increasing underlying rate of new infections. However, once programs are able to resume, there are ways to mitigate the impact and accelerate progress towards the 2030 goals.