Migration statistics relevant for malaria transmission in Senegal derived from mobile phone data and used in an agent-based migration model.

One year of mobile phone location data from Senegal is analysed to determine the characteristics of journeys that result in an overnight stay, and are thus relevant for malaria transmission. Defining the home location of each person as the place of most frequent calls, it is found that approximately 60% of people who spend nights away from home have regular destinations that are repeatedly visited, although only 10% have 3 or more regular destinations. The number of journeys involving overnight stays peaks at a distance of 50 km, although roughly half of such journeys exceed 100 km. Most visits only involve a stay of one or two nights away from home, with just 4% exceeding one week. A new agent-based migration model is introduced, based on a gravity model adapted to represent overnight journeys. Each agent makes journeys involving overnight stays to either regular or random locations, with journey and destination probabilities taken from the mobile phone dataset. Preliminary simulations show that the agent-based model can approximately reproduce the patterns of migration involving overnight stays

To what extent does climate explain variations in reported malaria cases in early 20th century Uganda?

Malaria case statistics were analysed for the period 1926 to 1960 to identify inter-annual variations in malaria cases for the Uganda Protectorate. The analysis shows the mid-to-late 1930s to be a period of increased reported cases. After World War II, malaria cases trend down to a relative minimum in the early 1950s, before increasing rapidly after 1953 to the end of the decade. Data for the Western Province confirm these national trends, which at the time were attributed to a wide range of causes, including land development and management schemes, population mobility, interventions and misdiagnosis. Climate was occasionally proposed as a contributor to enhanced case numbers, and unusual precipitation patterns were held responsible; temperature was rarely, if ever, considered. In this study, a dynamical malaria model was driven with available precipitation and temperature data from the period for five stations located across a range of environments in Uganda. In line with the historical data, the simulations produced relatively enhanced transmission in the 1930s, although there is considerable variability between locations. In all locations, malaria transmission was low in the late 1940s and early 1950s, steeply increasing after 1954. Results indicate that past climate variability explains some of the variations in numbers of reported malaria cases. The impact of multiannual variability in temperature, while only on the order of 0.5°C, was sufficient to drive some of the trends observed in the statistics and thus the role of climate was likely underestimated in the contemporary reports. As the elimination campaigns of the 1960s followed this partly climate-driven increase in malaria, this emphasises the need to account for climate when planning and evaluating intervention strategies

Emulation and History Matching using the hmer Package

Modelling complex real-world situations such as infectious diseases, geological phenomena, and biological processes can present a dilemma: the computer model (referred to as a simulator) needs to be complex enough to capture the dynamics of the system, but each increase in complexity increases the evaluation time of such a simulation, making it difficult to obtain an informative description of parameter choices that would be consistent with observed reality. While methods for identifying acceptable matches to real-world observations exist, for example optimisation or Markov chain Monte Carlo methods, they may result in non-robust inferences or may be infeasible for computationally intensive simulators. The techniques of emulation and history matching can make such determinations feasible, efficiently identifying regions of parameter space that produce acceptable matches to data while also providing valuable information about the simulator's structure, but the mathematical considerations required to perform emulation can present a barrier for makers and users of such simulators compared to other methods. The hmer package provides an accessible framework for using history matching and emulation on simulator data, leveraging the computational efficiency of the approach while enabling users to easily match to, visualise, and robustly predict from their complex simulators.Comment: 40 pages, 11 figures; submitted to Journal of Statistical Software: author order correcte

Estimating age-mixing patterns relevant for the transmission of airborne infections.

INTRODUCTION: Age-mixing patterns can have substantial effects on infectious disease dynamics and intervention effects. Data on close contacts (people spoken to and/or touched) are often used to estimate age-mixing. These are not the only relevant contacts for airborne infections such as tuberculosis, where transmission can occur between anybody 'sharing air' indoors. Directly collecting data on age-mixing patterns between casual contacts (shared indoor space, but not 'close') is difficult however. We demonstrate a method for indirectly estimating age-mixing patterns between casual indoor contacts from social contact data. METHODS: We estimated age-mixing patterns between close, casual, and all contacts using data from a social contact survey in South Africa. The age distribution of casual contacts in different types of location was estimated from the reported time spent in the location type by respondents in each age group. RESULTS: Patterns of age-mixing calculated from contact numbers were similar between close and all contacts, however patterns of age-mixing calculated from contact time were more age-assortative in all contacts than in close contacts. There was also more variation by age group in total numbers of casual and all contacts, than in total numbers of close contacts. Estimates were robust to sensitivity analyses. CONCLUSIONS: Patterns of age-mixing can be estimated for all contacts using data that can be easily collected as part of social contact surveys or time-use surveys, and may differ from patterns between close contacts

Evaluation of the role of location and distance in recruitment in respondent-driven sampling.

BACKGROUND: Respondent-driven sampling(RDS) is an increasingly widely used variant of a link tracing design for recruiting hidden populations. The role of the spatial distribution of the target population has not been robustly examined for RDS. We examine patterns of recruitment by location, and how they may have biased an RDS study findings. METHODS: Total-population data were available on a range of characteristics on a population of 2402 male household-heads from an open cohort of 25 villages in rural Uganda. The locations of households were known a-priori. An RDS survey was carried out in this population, employing current RDS methods of sampling and statistical inference. RESULTS: There was little heterogeneity in the population by location. Data suggested more distant contacts were less likely to be reported, and therefore recruited, but if reported more distant contacts were as likely as closer contacts to be recruited. There was no evidence that closer proximity to a village meeting place was associated with probability of being recruited, however it was associated with a higher probability of recruiting a larger number of recruits. People living closer to an interview site were more likely to be recruited. CONCLUSIONS: Household location affected the overall probability of recruitment, and the probability of recruitment by a specific recruiter. Patterns of recruitment do not appear to have greatly biased estimates in this study. The observed patterns could result in bias in more geographically heterogeneous populations. Care is required in RDS studies when choosing the network size question and interview site location(s)

A competency framework on simulation modelling-supported decision-making for Master of Public Health graduates.

BACKGROUND: Simulation models are increasingly important for supporting decision-making in public health. However, due to lack of training, many public health professionals remain unfamiliar with constructing simulation models and using their outputs for decision-making. This study contributes to filling this gap by developing a competency framework on simulation model-supported decision-making targeting Master of Public Health education. METHODS: The study combined a literature review, a two-stage online Delphi survey and an online consensus workshop. A draft competency framework was developed based on 28 peer-reviewed publications. A two-stage online Delphi survey involving 15 experts was conducted to refine the framework. Finally, an online consensus workshop, including six experts, evaluated the competency framework and discussed its implementation. RESULTS: The competency framework identified 20 competencies related to stakeholder engagement, problem definition, evidence identification, participatory system mapping, model creation and calibration and the interpretation and dissemination of model results. The expert evaluation recommended differentiating professional profiles and levels of expertise and synergizing with existing course contents to support its implementation. CONCLUSIONS: The competency framework developed in this study is instrumental to including simulation model-supported decision-making in public health training. Future research is required to differentiate expertise levels and develop implementation strategies

Comparison of indoor contact time data in Zambia and Western Cape, South Africa suggests targeting of interventions to reduce Mycobacterium tuberculosis transmission should be informed by local data.

BACKGROUND: In high incidence settings, the majority of Mycobacterium tuberculosis (M.tb) transmission occurs outside the household. Little is known about where people's indoor contacts occur outside the household, and how this differs between different settings. We estimate the number of contact hours that occur between adults and adult/youths and children in different building types in urban areas in Western Cape, South Africa, and Zambia. METHODS: Data were collected from 3206 adults using a cross-sectional survey, on buildings visited in a 24-h period, including building function, visit duration, and number of adults/youths and children (5-12 years) present. The mean numbers of contact hours per day by building function were calculated. RESULTS: Adults in Western Cape were more likely to visit workplaces, and less likely to visit shops and churches than adults in Zambia. Adults in Western Cape spent longer per visit in other homes and workplaces than adults in Zambia. More adults/youths were present at visits to shops and churches in Western Cape than in Zambia, and fewer at homes and hairdressers. More children were present at visits to shops in Western Cape than in Zambia, and fewer at schools and hairdressers. Overall numbers of adult/youth indoor contact hours were the same at both sites (35.4 and 37.6 h in Western Cape and Zambia respectively, p = 0.4). Child contact hours were higher in Zambia (16.0 vs 13.7 h, p = 0.03). Adult/youth and child contact hours were highest in workplaces in Western Cape and churches in Zambia. Compared to Zambia, adult contact hours in Western Cape were higher in workplaces (15.2 vs 8.0 h, p = 0.004), and lower in churches (3.7 vs 8.6 h, p = 0.002). Child contact hours were higher in other peoples' homes (2.8 vs 1.6 h, p = 0.03) and workplaces (4.9 vs 2.1 h, p = 0.003), and lower in churches (2.5 vs 6.2, p = 0.004) and schools (0.4 vs 1.5, p = 0.01). CONCLUSIONS: Patterns of indoor contact between adults and adults/youths and children differ between different sites in high M.tb incidence areas. Targeting public buildings with interventions to reduce M.tb transmission (e.g. increasing ventilation or UV irradiation) should be informed by local data

Estimating the contribution of transmission in primary healthcare clinics to community-wide TB disease incidence, and the impact of infection prevention and control interventions, in KwaZulu-Natal, South Africa.

BACKGROUND: There is a high risk of Mycobacterium tuberculosis (Mtb) transmission in healthcare facilities in high burden settings. WHO guidelines on tuberculosis (TB) infection prevention and control (IPC) recommend a range of measures to reduce transmission in healthcare settings. These were evaluated primarily based on evidence for their effects on transmission to healthcare workers in hospitals. To estimate the overall impact of IPC interventions, it is necessary to also consider their impact on community-wide TB incidence and mortality. METHODS: We developed an individual-based model of Mtb transmission in households, primary healthcare (PHC) clinics, and all other congregate settings. The model was parameterised using data from a high HIV prevalence community in South Africa, including data on social contact by setting, by sex, age, and HIV/antiretroviral therapy status; and data on TB prevalence in clinic attendees and the general population. We estimated the proportion of disease in adults that resulted from transmission in PHC clinics, and the impact of a range of IPC interventions in clinics on community-wide TB. RESULTS: We estimate that 7.6% (plausible range 3.9%-13.9%) of non-multidrug resistant and multidrug resistant TB in adults resulted directly from transmission in PHC clinics in the community in 2019. The proportion is higher in HIV-positive people, at 9.3% (4.8%-16.8%), compared with 5.3% (2.7%-10.1%) in HIV-negative people. We estimate that IPC interventions could reduce incident TB cases in the community in 2021-2030 by 3.4%-8.0%, and deaths by 3.0%-7.2%. CONCLUSIONS: A non-trivial proportion of TB results from transmission in clinics in the study community, particularly in HIV-positive people. Implementing IPC interventions could lead to moderate reductions in disease burden. We recommend that IPC measures in clinics should be implemented for their benefits to staff and patients, but also for their likely effects on TB incidence and mortality in the surrounding community

Time to change the way we think about tuberculosis infection prevention and control in health facilities: insights from recent research

In clinical settings where airborne pathogens, such as Mycobacterium tuberculosis, are prevalent, they constitute an important threat to health workers and people accessing healthcare. We report key insights from a 3-year project conducted in primary healthcare clinics in South Africa, alongside other recent tuberculosis infection prevention and control (TB-IPC) research. We discuss the fragmentation of TB-IPC policies and budgets; the characteristics of individuals attending clinics with prevalent pulmonary tuberculosis; clinic congestion and patient flow; clinic design and natural ventilation; and the facility-level determinants of the implementation (or not) of TB-IPC interventions. We present modeling studies that describe the contribution of M. tuberculosis transmission in clinics to the community tuberculosis burden and economic evaluations showing that TB-IPC interventions are highly cost-effective. We argue for a set of changes to TB-IPC, including better coordination of policymaking, clinic decongestion, changes to clinic design and building regulations, and budgeting for enablers to sustain implementation of TB-IPC interventions. Additional research is needed to find the most effective means of improving the implementation of TB-IPC interventions; to develop approaches to screening for prevalent pulmonary tuberculosis that do not rely on symptoms; and to identify groups of patients that can be seen in clinic less frequently

Improving ART programme retention and viral suppression are key to maximising impact of treatment as prevention - a modelling study.

BACKGROUND: UNAIDS calls for fewer than 500,000 new HIV infections/year by 2020, with treatment-as-prevention being a key part of their strategy for achieving the target. A better understanding of the contribution to transmission of people at different stages of the care pathway can help focus intervention services at populations where they may have the greatest effect. We investigate this using Uganda as a case study. METHODS: An individual-based HIV/ART model was fitted using history matching. 100 model fits were generated to account for uncertainties in sexual behaviour, HIV epidemiology, and ART coverage up to 2015 in Uganda. A number of different ART scale-up intervention scenarios were simulated between 2016 and 2030. The incidence and proportion of transmission over time from people with primary infection, post-primary ART-naïve infection, and people currently or previously on ART was calculated. RESULTS: In all scenarios, the proportion of transmission by ART-naïve people decreases, from 70% (61%-79%) in 2015 to between 23% (15%-40%) and 47% (35%-61%) in 2030. The proportion of transmission by people on ART increases from 7.8% (3.5%-13%) to between 14% (7.0%-24%) and 38% (21%-55%). The proportion of transmission by ART dropouts increases from 22% (15%-33%) to between 31% (23%-43%) and 56% (43%-70%). CONCLUSIONS: People who are currently or previously on ART are likely to play an increasingly large role in transmission as ART coverage increases in Uganda. Improving retention on ART, and ensuring that people on ART remain virally suppressed, will be key in reducing HIV incidence in Uganda