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Journal articlePetrie J, Hay JA, Srimokla O, et al., 2025,
Enhanced testing can substantially improve defense against several types of respiratory virus pandemic.
, Epidemics, Vol: 50Mass testing to identify and isolate infected individuals is a promising approach for reducing harm from the next acute respiratory virus pandemic. It offers the prospect of averting hospitalizations and deaths whilst avoiding the need for indiscriminate social distancing measures. To understand scenarios where mass testing might or might not be a viable intervention, here we modeled how effectiveness depends both on characteristics of the pathogen (R0, time to peak viral load) and on the testing strategy (limit of detection, testing frequency, test turnaround time, adherence). We base time-dependent test sensitivity and time-dependent infectiousness on an underlying viral load trajectory model. We show that given moderately high public adherence, frequent testing can prevent as many transmissions as more costly interventions such as school or business closures. With very high adherence and fast, frequent, and sensitive testing, we show that most respiratory virus pandemics could be controlled with mass testing alone.
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Journal articleHan SM, Shiino T, Masuda S, et al., 2025,
Phylogenetic Study of Local Patterns Influenza A(H3N2) Virus Transmission in a Semi-Isolated Population in a Remote Island in Japan Between 2011 and 2013.
, Influenza Other Respir Viruses, Vol: 19BACKGROUND: Influenza A outbreak risk is impacted by the potential for importation and local transmission. Reconstructing transmission history with phylogenetic analysis of genetic sequences can help assess outbreak risk but relies on regular collection of genetic sequences. Few influenza genetic sequences are collected in Japan, which makes phylogenetic analysis challenging, especially in rural, remote settings. We generated influenza A genetic sequences from nasopharyngeal swabs (NPS) samples collected using rapid influenza diagnostic tests and used them to analyze the transmission dynamics of influenza in a remote island in Japan. METHODS: We generated 229 whole genome sequences of influenza A/H3N2 collected during 2011/12 and 2012/13 influenza seasons in Kamigoto Island, Japan, of which 178 sequences passed the quality check. We built time-resolved phylogenetic trees from hemagglutinin sequences to classify the circulating clades by comparing the Kamigoto sequences to global sequences. Spatiotemporal transmission patterns were then analyzed for the largest local clusters. RESULTS: Using a time-resolved phylogenetic tree, we showed that the sequences clustered in six independent transmission groups (1 in 2011/12, 5 in 2012/13). Sequences were closely related to strains from mainland Japan. All 2011/12 strains were identified as clade 3C.2 (n = 29), while 2012/13 strains fell into two clades: clade 3C.2 (n = 129) and 3C.3a (n = 20). Clusters reported in 2012/13 circulated simultaneously in the same regions. The spatiotemporal analysis of the largest cluster revealed that while the first sequences were reported in the busiest district of Kamigoto, the later sequences were scattered across the island. CONCLUSION: Kamigoto Island was exposed to repeated importations of Influenza A(H3N2), mostly from mainland Japan, sometimes leading to local transmission and ultimately outbreaks. As independent groups of sequences overlapped
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Journal articleLeng T, 2025,
Potential impact and cost-effectiveness of oral HIV pre-exposure prophylaxis for men who have sex with men in Cotonou, Benin: a mathematical modelling study
, The Lancet Global Health, ISSN: 2214-109X -
Journal articleAvraam D, Hadjichrysanthou C, 2025,
The impact of contact-network structure on important epidemiological quantities of infectious disease transmission and the identification of the extremes.
, J Theor Biol, Vol: 599An individual-based stochastic model was developed to simulate the spread of an infectious disease in an SEIR-type system on all possible contact-networks of size between six and nine nodes. We assessed systematically the impact of the change in the population contact structure on four important epidemiological quantities: i) the epidemic duration, ii) the maximum number of infected individuals at a time point during the epidemic, iii) the time at which the maximum number of infected individuals is reached, and iv) the total number of individuals that have been infected during the epidemic. We considered the potential relationship of these quantities as the network changes and identified the networks that maximise and minimise each of these in the case of an epidemic outbreak. Chain-like networks minimise the peak and final epidemic size, but the disease spread is slow on such contact structures which leads to the maximisation of the epidemic duration. Star-like networks maximise the time to the peak whereas highly connected networks lead to faster disease transmission, and higher peak and final epidemic size. While the pairwise relationship of most of the quantities becomes almost linear, or inverse linear, as the network connectivity increases and approaches the complete network, the relationships are non-linear towards networks of low connectivity. In particular, the pairwise relationship between the final epidemic size and other quantities is changed in a 'bow-shaped' manner. There is a strong inverse linear relationship between epidemic duration and peak epidemic size with increasing network connectivity. The (inverse) linear relationships between quantities are more pronounced in cases of high disease transmissibility. All the values of the quantities change in a non-linear way with the increase of network connectivity and are characterised by high variability between networks of the same degree. The variability decreases as network connectivity increases.
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Journal articleCooper LV, Bandyopadhyay AS, Grassly NC, et al., 2025,
Global Impact of Mass Vaccination Campaigns on Circulating Type 2 Vaccine-Derived Poliovirus Outbreaks: An Interrupted Time-Series Analysis.
, J Infect Dis, Vol: 231, Pages: e446-e455BACKGROUND: Between 2016 and 2023, 3248 cases of circulating vaccine-derived type 2 poliomyelitis (cVDPV2) were reported globally and supplementary immunization activities (SIAs) with monovalent type 2 oral poliovirus vaccine (mOPV2) and novel type 2 oral poliovirus vaccine (nOPV2) targeted an estimated 356 and 525 million children, respectively. This analysis estimates the community-level impact of nOPV2 relative to mOPV2 SIAs. METHODS: We fitted interrupted time-series regressions to surveillance data between January 2016 and November 2023 to estimate the impact of nOPV2 and mOPV2 SIAs on cVDPV2 poliomyelitis incidence and prevalence in environmental surveillance across 37 countries, directly comparing the impact of SIAs in 13 countries where both vaccines were used. RESULTS: We did not find any statistically significant differences between nOPV2 and mOPV2 SIA impact except for in the Democratic Republic of Congo (DRC), where nOPV2 SIAs had lower impact (adjusted relative risk [aRR] for cVDPV2 poliomyelitis incidence per nOPV2 SIA, 0.505; 95% confidence interval [CI], .409-.623) compared to mOPV2 (aRR, 0.193; 95% CI, .137-.272); P value for difference in RRs = 3e-6. CONCLUSIONS: We find variation in OPV2 SIA impacts globally, with greater certainty about Nigeria and DRC, where large outbreaks provided an opportunity to assess impact at scale. In most countries, we find no significant difference between nOPV2 and mOPV2 SIA impact. We are unable to identify the reason for the significant difference in DRC, which could include differential SIA coverage, timing, vaccine effectiveness, or outbreak dynamics.
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Journal articleKraemer MUG, Tsui JLH, Chang SY, et al., 2025,
Artificial intelligence for modelling infectious disease epidemics
, Nature, Vol: 638, Pages: 623-635, ISSN: 0028-0836Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI.
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Journal articleMarie Brunnich Sloth M, Hruza J, Mortensen LH, et al., 2025,
Cause-specific mortality after spousal bereavement in a Danish register-based cohort
, Scientific Reports, ISSN: 2045-2322 -
Journal articleNg CW, Maddren R, Anderson RM, 2025,
Challenges in assessing the impact of infection and disease control interventions over the past decade based on the Expanded Special Project for the Elimination of Neglected Topical Diseases (ESPEN) database
, Transactions of The Royal Society of Tropical Medicine and Hygiene, ISSN: 0035-9203Over the past 2 decades there has been good progress in the control of many of the neglected tropical diseases (NTDs) treatable by preventative chemotherapy (PC). Continued major drug donations from pharmaceutical companies, support from philanthropic organizations and heightened international recognition of the health impacts of these diseases have each played an important role in lowering the global health burden due to NTDs. However, considerable improvement in data collection is required to accurately assess this progress as we move towards the ‘end game’ of eliminating these infections as a source of morbidity and mortality. The data quality, type and format collected by the Expanded Special Project for the Elimination of Neglected Tropical Diseases database from the African Ministries of Health are discussed and suggestions made for improvements in collection and presentation.
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Journal articleMcCabe R, Johnson LF, Whittles L, 2025,
Estimating the burden of mpox among MSM in South Africa
, BMJ Global Health, ISSN: 2059-7908 -
Journal articleNdovie W, Havránek J, Leconte J, et al., 2025,
Exploration of the genetic landscape of bacterial dsDNA viruses reveals an ANI gap amid extensive mosaicism.
, mSystems, Vol: 10Average nucleotide identity (ANI) is a widely used metric to estimate genetic relatedness, especially in microbial species delineation. While ANI calculation has been well optimized for bacteria and closely related viral genomes, accurate estimation of ANI below 80%, particularly in large reference data sets, has been challenging due to a lack of accurate and scalable methods. To bridge this gap, we introduce MANIAC, an efficient computational pipeline optimized for estimating ANI and alignment fraction (AF) in viral genomes with divergence around ANI of 70%. Using a rigorous simulation framework, we demonstrate MANIAC's accuracy and scalability compared to existing approaches, even to data sets of hundreds of thousands of viral genomes. Applying MANIAC to a curated data set of complete bacterial dsDNA viruses revealed a multimodal ANI distribution, with a distinct gap around 80%, akin to the bacterial ANI gap (~90%) but shifted, likely due to viral-specific evolutionary processes such as recombination dynamics and mosaicism. We then evaluated ANI and AF as predictors of genus-level taxonomy using a logistic regression model. We found that this model has strong predictive power (PR-AUC = 0.981), but that it works much better for virulent (PR-AUC = 0.997) than temperate (PR-AUC = 0.847) bacterial viruses. This highlights the complexity of taxonomic classification in temperate phages, known for their extensive mosaicism, and cautions against over-reliance on ANI in such cases. MANIAC can be accessed at https://github.com/bioinf-mcb/MANIAC.IMPORTANCEWe introduce a novel computational pipeline called MANIAC, designed to accurately assess average nucleotide identity (ANI) and alignment fraction (AF) between diverse viral genomes, scalable to data sets of over 100k genomes. Using computer simulations and real data analyses, we show that MANIAC could accurately estimate genetic relatedness between pairs of viral genomes of around 60%-70% ANI. We applied MANIAC to investiga
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