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• Journal article
  She B, Mangal TD, Prust ML, Heung S, Chalkley M, Colbourn T, Collins JH, Graham MM, Jewell B, Joshi P, Li Lin I, Mnjowe E, Mohan S, Molaro M, Phillips AN, Revill P, Smith RM, Tamuri AU, Twea PD, Manthalu G, Mfutso-Bengo J, Hallett TBet al., 2024,

  Health workforce needs in Malawi: analysis of the Thanzi La Onse integrated epidemiological model of care

  , Human Resources for Health, Vol: 22, ISSN: 1478-4491

  Background: To make the best use of health resources, it is crucial to understand the healthcare needsof a population – including how needs will evolve and respond to changing epidemiological context and patient behaviour – and how this compares to the capabilities to deliver healthcare with the existing workforce. Existing approaches to planning either rely on using observed healthcare demandfrom a fixed historical period or using models to estimate healthcare needs within a narrow domain(e.g., a specific diease area or health programme). A new data-grounded modelling method is proposed by which healthcare needs and the capabilities of the healthcare workforce can be compared and analysed under a range of scenarios: in particular, when there is much greater propensity for healthcare seeking.Methods: A model representation of the healthcare workforce, one that formalises how the time of the different cadres is drawn into the provision of units of healthcare, was integrated with an individual-based epidemiological model - the Thanzi La Onse Model - that represents mechanistically the development of disease and ill-health and patients’ healthcare seeking behaviour. The model was applied in Malawi using routinely available data and the estimates of the volume of health servicedelivered were tested against officially recorded data. Model estimates of the “time needed” and “time available” for each cadre were compared under different assumptions for whether vacant (or established) posts are filled and healthcare seeking behaviour.Results: The model estimates of volume of each type of service delivered were in good agreement with the available data. The “time needed” for the healthcare workforce greatly exceeded the “time available” (overall by 1.82-fold), especially for pharmacists (6.37-fold) and clinicians (2.83-fold). This discrepancy would be largely mitigated if all vacant posts were filled, but the l

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• Journal article
  Mayer J, Collyer BS, Maddren R, Abtew B, Liyew EF, Chernet M, Tollera G, Tasew G, Anderson RMet al., 2024,

  Patterns of soil-transmitted helminth aggregation in the human host population after several years of intensive mass drug administration.

  , Trans R Soc Trop Med Hyg

  BACKGROUND: Community-wide mass drug administration (cMDA) is known as an effective, albeit costly, control strategy for soil-transmitted helminth (STH) parasites. A better understanding of STH aggregation after many rounds of cMDA could help shape more cost-effective policies. METHODS: This analysis uses data from the Geshiyaro project, aiming to break STH transmission by cMDA and water, sanitation and hygiene interventions. Ascaris lumbricoides infection prevalence is derived from egg count data and parasite aggregation is obtained by fitting a negative binomial distribution to the frequency distribution of faecal egg counts. RESULTS: The relationship between parasite dispersion and infection prevalence is approximately linear. Parasite aggregation increases as infection prevalence decreases. CONCLUSIONS: A minority of individuals carry most parasites as prevalence decreases in the community. These individuals could be selectively targeted for repeated treatment.

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• Journal article
  Melnychuk S, Balakireva O, Pavlova D, Lopatenko A, McClarty LM, Lazarus L, Herpai N, Pickles M, Mishra S, Becker ML, Sandstrom P, Cholette Fet al., 2024,

  Joint HIV and hepatitis C virus phylogenetic analyses signal network overlap among women engaged in sex work and men who purchase sex

  , International Journal of STD & AIDS, ISSN: 0956-4624

  <jats:sec><jats:title>Background</jats:title><jats:p> Transmission of HIV and hepatitis C virus (HCV) are heavily influenced by complex interactions within sexual or injecting networks where risk behaviors occur. In Ukraine, women engaged in sex work (WSW) and men who purchase sex (MWPS) are disproportionately affected by both viruses. The aim of our study was to the investigate the influence of underlying networks on transmission of HIV and HCV. </jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p> A cross-sectional integrated bio-behavioural survey was implemented among 560 WSW and 370 MWPS representative of sex work hotspots in Dnipro, Ukraine (December 2017 to March 2018). A portion of the HIV reverse transcriptase gene ( n = 13; 62% WSW, 38% MWPS) and HCV NS5B gene ( n = 46; 70% WSW, 30% MWPS) were sequenced from dried blood spot specimens. Tip-to-tip distances on phylogenetic trees were used to infer phylogenetic clusters for identifying potential transmission clusters. </jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p> Phylogenetic analyses identified two HIV clusters containing four sequences (50% WSW; 50% MWPS) and 11 HCV clusters containing 31 sequences – the majority comprising infections in WSW (83.9%). Nearly half (45.4%) of HCV clusters contained at least one WSW with a history of injecting drugs. </jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p> Joint analyses of HIV and HCV signal overlap in sex work and injecting networks in Ukraine, suggesting implications for the comprehensive coverage of prevention programs for WSW including harm reduction services. Conducting phylogenetic analyses with HCV may provide a more complete appraisal of underlying transmission networks than HIV alone, particularly in the context of high HIV treatment coverage yielding vira

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• Journal article
  Jorgensen D, Grassly NC, Pons Salort M, 2024,

  Global age-stratified seroprevalence of enterovirus D68: a systematic literature review

  , The Lancet Microbe, ISSN: 2666-5247

  Enterovirus D68 (EV-D68), first isolated in 1962, emerged in 2014, causing outbreaks of severe respiratory infections and acute flaccid myelitis. In this systematic review, we have compiled all available literature on age-stratified seroprevalence estimates of EV-D68. Ten studies from six countries were retained, all conducted using microneutralisation assays, despite wide variations in protocols and challenge viruses. The age profiles of seroprevalence were similar across time and regions; seroprevalence increased quickly with age, reaching roughly 100% by the age of 20 years and with no sign of decline throughout adulthood. This suggests continuous or frequent exposure of the populations to the virus, or possible cross-reactivity with other viruses. Studies with two or more cross-sectional surveys reported consistently higher seroprevalence at later timepoints, suggesting a global increase in transmission over time. This systematic review concludes that standardising serological protocols, understanding the contribution of cross-reactivity with other pathogens to the high reported seroprevalence, and quantifying individual exposure to EV-D68 over time are the main research priorities for the future.

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• Journal article
  Boily M-C, 2024,

  From conceptualising to modelling structural determinants and interventions in HIV transmission dynamics models: a scoping review and methodological framework for evidence-based analyses

  , BMC Medicine, Vol: 22, ISSN: 1741-7015

  BackgroundIncluding structural determinants (e.g. criminalisation, stigma, inequitable gender norms) in dynamic HIV transmission models is important to help quantify their population-level impacts and guide implementation of effective interventions that reduce the burden of HIV and inequalities thereof. However, evidence-based modelling of structural determinants is challenging partly due to a limited understanding of their causal pathways and few empirical estimates of their effects on HIV acquisition and transmission.MethodsWe conducted a scoping review of dynamic HIV transmission modelling studies that evaluated the impacts of structural determinants, published up to August 28, 2023, using Ovid Embase and Medline online databases. We appraised studies on how models represented exposure to structural determinants and causal pathways. Building on this, we developed a new methodological framework and recommendations to support the incorporation of structural determinants in transmission dynamics models and their analyses. We discuss the data and analyses that could strengthen the evidence used to inform these models.ResultsWe identified 17 HIV modelling studies that represented structural determinants and/or interventions, including incarceration of people who inject drugs (number of studies [n] = 5), violence against women (n = 3), HIV stigma (n = 1), and housing instability (n = 1), among others (n = 7). Most studies (n = 10) modelled exposures dynamically. Almost half (8/17 studies) represented multiple exposure histories (e.g. current, recent, non-recent exposure). Structural determinants were often assumed to influence HIV indirectly by influencing mediators such as contact patterns, condom use, and antiretroviral therapy use. However, causal pathways’ assumptions were sometimes simple, with few mediators explicitly represented in the model, and largely based on cross-sect

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• Journal article
  Arinaminpathy N, Reed C, Biggerstaff M, Nguyen AT, Athni TS, Arnold BF, Hubbard A, Reingold A, Benjamin-Chung Jet al., 2024,

  Estimating community-wide indirect effects of influenza vaccination: triangulation using mathematical models and bias analysis.

  , Am J Epidemiol

  Understanding whether influenza vaccine promotion strategies produce community-wide indirect effects is important for establishing vaccine coverage targets and optimizing vaccine delivery. Empirical epidemiologic studies and mathematical models have been used to estimate indirect effects of vaccines but rarely for the same estimand in the same dataset. Using these approaches together could be a powerful tool for triangulation in infectious disease epidemiology because each approach is subject to distinct sources of bias. We triangulated evidence about indirect effects from a school-located influenza vaccination program using two approaches: a difference-in-difference (DID) analysis, and an age-structured, deterministic, compartmental model. The estimated indirect effect was substantially lower in the mathematical model than in the DID analysis (2.1% (95% Bayesian credible intervals 0.4 - 4.4%) vs. 22.3% (95% CI 7.6% - 37.1%)). To explore reasons for differing estimates, we used sensitivity analyses and probabilistic bias analyses. When we constrained model parameters such that projections matched the DID analysis, results only aligned with the DID analysis with substantially lower pre-existing immunity among school-age children and older adults. Conversely, DID estimates corrected for potential bias only aligned with mathematical model estimates under differential outcome misclassification. We discuss how triangulation using empirical and mathematical modelling approaches could strengthen future studies.

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• Journal article
  Menkir TF, Citarella BW, Sigfrid L, Doshi Y, Reyes LF, Calvache JA, Kildal AB, Nygaard AB, Holter JC, Panda PK, Jassat W, Merson L, Donnelly CA, Santillana M, Buckee C, Verguet S, Hejazi NS, ISARIC Clinical Characterisation Groupet al., 2024,

  Modeling the relative influence of socio-demographic variables on post-acute COVID-19 quality of life.

  , medRxiv

  IMPORTANCE: Post-acute sequelae of SARS-CoV-2, referred to as "long COVID", are a globally pervasive threat. While their many clinical determinants are commonly considered, their plausible social correlates are often overlooked. OBJECTIVE: To compare social and clinical predictors of differences in quality of life (QoL) with long COVID. Additionally, to measure how much adjusted associations between social factors and long COVID-associated quality of life are unexplained by important clinical intermediates. DESIGN SETTING AND PARTICIPANTS: Data from the ISARIC long COVID multi-country prospective cohort study. Subjects from Norway, the United Kingdom (UK), and Russia, aged 16 and above, with confirmed acute SARS-CoV-2 infection reporting >= 1 long COVID-associated symptoms 1+ month following infection. EXPOSURE: The social exposures considered were educational attainment (Norway), employment status (UK and Russia), and female vs male sex (all countries). MAIN OUTCOME AND MEASURES: Quality of life-adjusted days, or QALDs, with long COVID. RESULTS: This cohort study included a total of 3891 participants. In all three countries, educational attainment, employment status, and female sex were important predictors of long COVID QALDs. Furthermore, a majority of the estimated relationships between each of these social correlates and long COVID QALDs could not be attributed to key long COVID-predicting comorbidities. In Norway, 90% (95% CI: 77%, 100%) of the adjusted association between the top two quintiles of educational attainment and long COVID QALDs was not explained by clinical intermediates. The same was true for 86% (73%, 100%) and 93% (80%,100%) of the adjusted associations between full-time employment and long COVID QALDs in the United Kingdom (UK) and Russia. Additionally, 77% (46%,100%) and 73% (52%, 94%) of the adjusted associations between female sex and long COVID QALDs in Norway and the UK were unexplained by the clinical mediators. CONCLUSIONS

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• Journal article
  Grant R, Rubin M, Abbas M, Pittet D, Srinivasan A, Jernigan JA, Bell M, Samore M, Harbarth S, Slayton RB, 2023 IPC Think Tank participantset al., 2024,

  Expanding the use of mathematical modeling in healthcare epidemiology and infection prevention and control.

  , Infect Control Hosp Epidemiol, Pages: 1-6

  During the coronavirus disease 2019 pandemic, mathematical modeling has been widely used to understand epidemiological burden, trends, and transmission dynamics, to facilitate policy decisions, and, to a lesser extent, to evaluate infection prevention and control (IPC) measures. This review highlights the added value of using conventional epidemiology and modeling approaches to address the complexity of healthcare-associated infections (HAI) and antimicrobial resistance. It demonstrates how epidemiological surveillance data and modeling can be used to infer transmission dynamics in healthcare settings and to forecast healthcare impact, how modeling can be used to improve the validity of interpretation of epidemiological surveillance data, how modeling can be used to estimate the impact of IPC interventions, and how modeling can be used to guide IPC and antimicrobial treatment and stewardship decision-making. There are several priority areas for expanding the use of modeling in healthcare epidemiology and IPC. Importantly, modeling should be viewed as complementary to conventional healthcare epidemiological approaches, and this requires collaboration and active coordination between IPC, healthcare epidemiology, and mathematical modeling groups.

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  Doohan P, Jorgensen D, Naidoo T, McCain K, Hicks J, McCabe R, Bhatia S, Charniga K, Cuomo-Dannenburg G, Hamlet A, Nash R, Nikitin D, Rawson T, Sheppard R, Unwin H, van Elsland S, Cori A, Morgenstern C, Imai Net al., 2024,

  Lassa fever outbreaks, mathematical models, and disease parameters: a systematic review and meta-analysis

  , The Lancet Global Health, ISSN: 2214-109X
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• Journal article
  Merson L, Duque S, Garcia-Gallo E, Yeabah TO, Rylance J, Diaz J, Flahault A, Abdalasalam S, Abdalhadi AA, Abdalla W, Abdalla NR, Abdalrheem AH, Abdalsalam A, Abdeewi S, Abdelgaum EH, Abdelhalim M, Abdelkabir M, Abdukahil SA, Abdulbaqi LA, Abdulhamid W, Abdulhamid S, Abdulkadir NN, Abdulwahed E, Abdunabi R, Abe R, Abel L, Abodina AM, Abouelmagd K, Abrous A, Abu Jabal K, Abu Salah N, Abukhalaf SMA, Abusalama A, Abuzaid TA, Acharya S, Acker A, Adem S, Ademnou M, Adewhajah F, Adhikari NKJ, Adrião D, Yaw Adu S, Afum-Adjei Awuah A, Agbogbatey M, Ageel SA, Ahmed MM, Ahmed AM, Ahmed S, Alaraji ZA, Ahmed Elhefnawy Enan A, Abdelhamid Ahmed Khalil R, Ahmed Mohamed Abdelaziz AM, Aiello M, Ainscough K, Airlangga E, Aisa T, Aisha A, Aisha B, Hssain AA, Akimoto T, Akmal E, Akwani C, Qasim EA, Alaa Y, Alajeeli A, Alali A, Alalqam R, Alameen AM, Al-Aquily M, Alaraji ZA, Albakry K, Albatni S, Alberti A, Al-Dabbous T, Aldhalia A, Aldoukali A, Alessi M, Alex B, Alexandre K, Al-Fares A, Alflite A, Alfoudri H, Alfroukh KMA, Alhadad Q, Alhaddad HS, Alhasan MKMA, Alhouri AN, Alhouri H, Ali A, Ali MTM, Ali I, Abbas SA, Abdelghafar YA, Shah NA, Alidjnou KE, Aljadi M, Aljamal S, Alkahlout M, Alkaraki KJK, Alkaseek Aet al., 2024,

  Optimising Clinical Epidemiology in Disease Outbreaks: Analysis of ISARIC-WHO COVID-19 Case Report Form Utilisation

  , Epidemiologia, Vol: 5, Pages: 557-580

  Standardised forms for capturing clinical data promote consistency in data collection and analysis across research sites, enabling faster, higher-quality evidence generation. ISARIC and the World Health Organization have developed case report forms (CRFs) for the clinical characterisation of several infectious disease outbreaks. To improve the design and quality of future forms, we analysed the inclusion and completion rates of the 243 fields on the ISARIC-WHO COVID-19 CRF. Data from 42 diverse collaborations, covering 1886 hospitals and 950,064 patients, were analysed. A mean of 129.6 fields (53%) were included in the adapted CRFs implemented across the sites. Consistent patterns of field inclusion and completion aligned with globally recognised research priorities in outbreaks of novel infectious diseases. Outcome status was the most highly included (95.2%) and completed (89.8%) field, followed by admission demographics (79.1% and 91.6%), comorbidities (77.9% and 79.0%), signs and symptoms (68.9% and 78.4%), and vitals (70.3% and 69.1%). Mean field completion was higher in severe patients (70.2%) than in all patients (61.6%). The results reveal how clinical characterisation CRFs can be streamlined to reduce data collection time, including the modularisation of CRFs, to offer a choice of data volume collection and the separation of critical care interventions. This data-driven approach to designing CRFs enhances the efficiency of data collection to inform patient care and public health response.

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