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Journal articleHui ST, Gifford H, Rhodes J, 2024,
Emerging Antifungal Resistance in Fungal Pathogens
, Current Clinical Microbiology Reports, Vol: 11, Pages: 43-50Purpose of Review: Over recent decades, the number of outbreaks caused by fungi has increased for humans, plants (including important crop species) and animals. Yet this problem is compounded by emerging antifungal drug resistance in pathogenic species. Resistance develops over time when fungi are exposed to drugs either in the patient or in the environment. Recent Findings: Novel resistant variants of fungal pathogens that were previously susceptible are evolving (such as Aspergillus fumigatus) as well as newly emerging fungal species that are displaying antifungal resistance profiles (e.g. Candida auris and Trichophyton indotineae). Summary: This review highlights the important topic of emerging antifungal resistance in fungal pathogens and how it evolved, as well as how this relates to a growing public health burden.
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Journal articleVicco A, McCormack C, Pedrique B, et al., 2024,
A scoping literature review of global dengue age-stratified seroprevalence data: estimating dengue force of infection in endemic countries
, EBioMedicine, Vol: 104, ISSN: 2352-3964BackgroundDengue poses a significant burden worldwide, and a more comprehensive understanding of the heterogeneity in the intensity of dengue transmission within endemic countries is necessary to evaluate the potential impact of public health interventions.MethodsThis scoping literature review aimed to update a previous study of dengue transmission intensity by collating global age-stratified dengue seroprevalence data published in the Medline, Embase and Web of Science databases from 2014 to 2023. These data were then utilised to calibrate catalytic models and estimate the force of infection (FOI), which is the yearly per-capita risk of infection for a typical susceptible individual.FindingsWe found a total of 66 new publications containing 219 age-stratified seroprevalence datasets across 30 endemic countries. Together with the previously available average FOI estimates, there are now more than 250 dengue average FOI estimates obtained from seroprevalence studies from across the world.InterpretationThe results show large heterogeneities in average dengue FOI both across and within countries. These new estimates can be used to inform ongoing modelling efforts to improve our understanding of the drivers of the heterogeneity in dengue transmission globally, which in turn can help inform the optimal implementation of public health interventions.FundingUK Medical Research Council, Wellcome Trust, Community Jameel, Drugs for Neglected Disease initiative (DNDi) funded by the French Development Agency, Médecins Sans Frontières International; Swiss Agency for Development and Cooperation and UK aid.
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Journal articleNunes MC, Thommes E, Fröhlich H, et al., 2024,
Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report.
, Infect Dis Model, Vol: 9, Pages: 501-518In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes the rich discussions that occurred during the workshop. The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness. Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness.
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Journal articleMcCabe R, Danelian G, Panovska-Griffiths J, et al., 2024,
Inferring community transmission of SARS-CoV-2 in the United Kingdom using the ONS COVID-19 Infection Survey.
, Infect Dis Model, Vol: 9, Pages: 299-313Key epidemiological parameters, including the effective reproduction number, R(t), and the instantaneous growth rate, r(t), generated from an ensemble of models, have been informing public health policy throughout the COVID-19 pandemic in the four nations of the United Kingdom of Great Britain and Northern Ireland (UK). However, estimation of these quantities became challenging with the scaling down of surveillance systems as part of the transition from the "emergency" to "endemic" phase of the pandemic. The Office for National Statistics (ONS) COVID-19 Infection Survey (CIS) provided an opportunity to continue estimating these parameters in the absence of other data streams. We used a penalised spline model fitted to the publicly-available ONS CIS test positivity estimates to produce a smoothed estimate of the prevalence of SARS-CoV-2 positivity over time. The resulting fitted curve was used to estimate the "ONS-based" R(t) and r(t) across the four nations of the UK. Estimates produced under this model are compared to government-published estimates with particular consideration given to the contribution that this single data stream can offer in the estimation of these parameters. Depending on the nation and parameter, we found that up to 77% of the variance in the government-published estimates can be explained by the ONS-based estimates, demonstrating the value of this singular data stream to track the epidemic in each of the four nations. We additionally find that the ONS-based estimates uncover epidemic trends earlier than the corresponding government-published estimates. Our work shows that the ONS CIS can be used to generate key COVID-19 epidemiological parameters across the four UK nations, further underlining the enormous value of such population-level studies of infection. This is not intended as an alternative to ensemble modelling, rather it is intended as a potential solution to the aforementioned challenge faced by public h
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Journal articleMohan S, Mangal TD, Colbourn T, et al., 2024,
Factors associated with medical consumable availability in level 1 facilities in Malawi: a secondary analysis of a facility census.
, Lancet Glob Health, Vol: 12, Pages: e1027-e1037BACKGROUND: Medical consumable stock-outs negatively affect health outcomes not only by impeding or delaying the effective delivery of services but also by discouraging patients from seeking care. Consequently, supply chain strengthening is being adopted as a key component of national health strategies. However, evidence on the factors associated with increased consumable availability is limited. METHODS: In this study, we used the 2018-19 Harmonised Health Facility Assessment data from Malawi to identify the factors associated with the availability of consumables in level 1 facilities, ie, rural hospitals or health centres with a small number of beds and a sparsely equipped operating room for minor procedures. We estimate a multilevel logistic regression model with a binary outcome variable representing consumable availability (of 130 consumables across 940 facilities) and explanatory variables chosen based on current evidence. Further subgroup analyses are carried out to assess the presence of effect modification by level of care, facility ownership, and a categorisation of consumables by public health or disease programme, Malawi's Essential Medicine List classification, whether the consumable is a drug or not, and level of average national availability. FINDINGS: Our results suggest that the following characteristics had a positive association with consumable availability-level 1b facilities or community hospitals had 64% (odds ratio [OR] 1·64, 95% CI 1·37-1·97) higher odds of consumable availability than level 1a facilities or health centres, Christian Health Association of Malawi and private-for-profit ownership had 63% (1·63, 1·40-1·89) and 49% (1·49, 1·24-1·80) higher odds respectively than government-owned facilities, the availability of a computer had 46% (1·46, 1·32-1·62) higher odds than in its absence, pharmacists managing drug orders had 85% (1·85, 1·40-2&
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Journal articleKura K, Mutono N, Basáñez M-G, et al., 2024,
How does treatment coverage and proportion never treated influence the success of Schistosoma mansoni elimination as a public health problem by 2030?
, Clinical Infectious Diseases, Vol: 78, Pages: S126-S130, ISSN: 1058-4838BackgroundThe 2030 target for schistosomiasis is elimination as a public health problem (EPHP), achieved when the prevalence of heavy-intensity infection among school-aged children (SAC) reduces to <1%. To achieve this, the new World Health Organization guidelines recommend a broader target of population to include pre-SAC and adults. However, the probability of achieving EPHP should be expected to depend on patterns in repeated uptake of mass drug administration by individuals.MethodsWe employed 2 individual-based stochastic models to evaluate the impact of school-based and community-wide treatment and calculated the number of rounds required to achieve EPHP for Schistosoma mansoni by considering various levels of the population never treated (NT). We also considered 2 age-intensity profiles, corresponding to a low and high burden of infection in adults.ResultsThe number of rounds needed to achieve this target depends on the baseline prevalence and the coverage used. For low- and moderate-transmission areas, EPHP can be achieved within 7 years if NT ≤10% and NT <5%, respectively. In high-transmission areas, community-wide treatment with NT <1% is required to achieve EPHP.ConclusionsThe higher the intensity of transmission, and the lower the treatment coverage, the lower the acceptable value of NT becomes. Using more efficacious treatment regimens would permit NT values to be marginally higher. A balance between target treatment coverage and NT values may be an adequate treatment strategy depending on the epidemiological setting, but striving to increase coverage and/or minimize NT can shorten program duration.
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Journal articleKura K, Stolk WA, Basáñez M-G, et al., 2024,
How does the proportion of never treatment influence the success of mass drug administration programs for the elimination of lymphatic filariasis?
, Clinical Infectious Diseases, Vol: 78, Pages: S93-S100, ISSN: 1058-4838BackgroundMass drug administration (MDA) is the cornerstone for the elimination of lymphatic filariasis (LF). The proportion of the population that is never treated (NT) is a crucial determinant of whether this goal is achieved within reasonable time frames.MethodsUsing 2 individual-based stochastic LF transmission models, we assess the maximum permissible level of NT for which the 1% microfilaremia (mf) prevalence threshold can be achieved (with 90% probability) within 10 years under different scenarios of annual MDA coverage, drug combination and transmission setting.ResultsFor Anopheles-transmission settings, we find that treating 80% of the eligible population annually with ivermectin + albendazole (IA) can achieve the 1% mf prevalence threshold within 10 years of annual treatment when baseline mf prevalence is 10%, as long as NT <10%. Higher proportions of NT are acceptable when more efficacious treatment regimens are used. For Culex-transmission settings with a low (5%) baseline mf prevalence and diethylcarbamazine + albendazole (DA) or ivermectin + diethylcarbamazine + albendazole (IDA) treatment, elimination can be reached if treatment coverage among eligibles is 80% or higher. For 10% baseline mf prevalence, the target can be achieved when the annual coverage is 80% and NT ≤15%. Higher infection prevalence or levels of NT would make achieving the target more difficult.ConclusionsThe proportion of people never treated in MDA programmes for LF can strongly influence the achievement of elimination and the impact of NT is greater in high transmission areas. This study provides a starting point for further development of criteria for the evaluation of NT.
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Journal articleTurner H, Kura K, Roth B, et al., 2024,
An updated economic assessment of moxidectin treatment strategies for onchocerciasis elimination
, Clinical Infectious Diseases, Vol: 78, Pages: S138-S145, ISSN: 1058-4838Background:Concerns that annual mass administration of ivermectin, the predominant strategy for onchocerciasis control/elimination, may not lead to elimination of parasite transmission (EoT) in all endemic areas, has increased interest in alternative treatment strategies. One such strategy is moxidectin. We performed an updated economic assessment of moxidectin- relative to ivermectin-based strategies.Methods:We investigated annual and biannual community-directed treatment with ivermectin (aCDTI, bCDTI) and moxidectin (aCDTM, bCDTM) implemented with minimal or enhanced coverage (65% or 80% of the total population taking the drug, respectively) in intervention-naïve areas with 30%, 50% or 70% microfilarial baseline prevalence (representative of hypo-, meso- and hyperendemic areas). We compared programmatic delivery costs for the number of treatments achieving 90% probability of EoT (EoT90), calculated with the individual-based stochastic transmission model EPIONCHO-IBM. We used the costs for 40 years of programme delivery when EoT90 was not reached earlier. Delivery costs do not include the drug costs. Results:aCDTM and bCDTM achieved EoT90 with lower programmatic delivery costs than aCDTI with one exception: aCDTM with minimal coverage did not achieve EoT90 in hyperendemic areas within 40 years. With minimal coverage, bCDTI delivery costs as much or more than aCDTM and bCDTM. With enhanced coverage, programmatic delivery costs for aCDTM and bCDTM were lower than for aCDTI and bCDTI. Conclusions:Moxidectin-based strategies could accelerate progress towards EoT and reduce programmatic delivery costs compared to ivermectin-based strategies. The costs of moxidectin to national programmes are needed to quantify whether delivery cost reductions will translate into overall programme cost reduction.
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Journal articleVasconcelos A, King JD, Nunes-Alves C, et al., 2024,
Accelerating progress towards the 2030 neglected tropical diseases targets: how can quantitative modeling support programmatic decisions?
, Clinical Infectious Diseases, Vol: 78, Pages: S83-S92, ISSN: 1058-4838Over 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.
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Journal articleOgi-Gittins I, Hart WS, Song J, et al., 2024,
A simulation-based approach for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data.
, Epidemics, Vol: 47Tracking pathogen transmissibility during infectious disease outbreaks is essential for assessing the effectiveness of public health measures and planning future control strategies. A key measure of transmissibility is the time-dependent reproduction number, which has been estimated in real-time during outbreaks of a range of pathogens from disease incidence time series data. While commonly used approaches for estimating the time-dependent reproduction number can be reliable when disease incidence is recorded frequently, such incidence data are often aggregated temporally (for example, numbers of cases may be reported weekly rather than daily). As we show, commonly used methods for estimating transmissibility can be unreliable when the timescale of transmission is shorter than the timescale of data recording. To address this, here we develop a simulation-based approach involving Approximate Bayesian Computation for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data. We first use a simulated dataset representative of a situation in which daily disease incidence data are unavailable and only weekly summary values are reported, demonstrating that our method provides accurate estimates of the time-dependent reproduction number under such circumstances. We then apply our method to two outbreak datasets consisting of weekly influenza case numbers in 2019-20 and 2022-23 in Wales (in the United Kingdom). Our simple-to-use approach will allow accurate estimates of time-dependent reproduction numbers to be obtained from temporally aggregated data during future infectious disease outbreaks.
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