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Journal articlePaschoalotto MAC, Cima J, Costa E, et al., 2024,
Politics and confidence toward the COVID-19 vaccination: A Brazilian cross-sectional study.
, Hum Vaccin Immunother, Vol: 20This study has the aim of assessing the Brazilian perceptions, influencing factors and political positioning on the confidence concerning COVID-19 vaccination. To achieve the objective, the methods rely on a cross-sectional survey of Brazilian citizens, distributed through different social networks. The sample is composed of 1,670 valid responses, collected from almost all Brazilian states and state capitals. To analyze the data and give a clear view of the variables' relationship, the study used bivariate and comparative graphs. Results show a higher level of confidence in vaccines from Pfizer and AstraZeneca, while the lower level of confidence is associated with vaccines from Sinopharm and Sputinik5. Vaccine efficacy is the most significant influencing factor that helps in the decision to get vaccinated. Also, individuals are less willing to get vaccinated if their political preferences are related to the right-wing. The results led to three main health and social implications: i) the vaccination strategy campaigns should take in count vaccine efficacy and political aspects; ii) the vaccination process should be adapted to regions with different political positions; and iii) a reinforcement in the educational policies of the vaccine's importance to the public health, to avoid the politization of a health issue.
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Journal articleKwok KO, Huynh T, Wei WI, et al., 2024,
Utilizing large language models in infectious disease transmission modelling for public health preparedness.
, Comput Struct Biotechnol J, Vol: 23, Pages: 3254-3257, ISSN: 2001-0370INTRODUCTION: OpenAI's ChatGPT, a Large Language Model (LLM), is a powerful tool across domains, designed for text and code generation, fostering collaboration, especially in public health. Investigating the role of this advanced LLM chatbot in assisting public health practitioners in shaping disease transmission models to inform infection control strategies, marks a new era in infectious disease epidemiology research. This study used a case study to illustrate how ChatGPT collaborates with a public health practitioner in co-designing a mathematical transmission model. METHODS: Using natural conversation, the practitioner initiated a dialogue involving an iterative process of code generation, refinement, and debugging with ChatGPT to develop a model to fit 10 days of prevalence data to estimate two key epidemiological parameters: i) basic reproductive number (Ro) and ii) final epidemic size. Verification and validation processes are conducted to ensure the accuracy and functionality of the final model. RESULTS: ChatGPT developed a validated transmission model which replicated the epidemic curve and gave estimates of Ro of 4.19 (95 % CI: 4.13- 4.26) and a final epidemic size of 98.3 % of the population within 60 days. It highlighted the advantages of using maximum likelihood estimation with Poisson distribution over least squares method. CONCLUSION: Integration of LLM in medical research accelerates model development, reducing technical barriers for health practitioners, democratizing access to advanced modeling and potentially enhancing pandemic preparedness globally, particularly in resource-constrained populations.
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Journal articleSchnizer M, Schellong P, Rose N, et al., 2024,
Long versus short course anti-microbial therapy of uncomplicated Staphylococcus aureus bacteraemia: a systematic review.
, Clin Microbiol Infect, Vol: 30, Pages: 1254-1260BACKGROUND: Current guidelines recommend at least 2 weeks duration of antibiotic therapy (DOT) for patients with uncomplicated Staphylococcus aureus bacteraemia (SAB) but the evidence for this recommendation is unclear. OBJECTIVES: To perform a systematic literature review assessing current evidence for recommended DOT for patients with SAB. METHODS: The following are the methods used for this study. DATA SOURCES: We searched MEDLINE, ISI Web of Science, the Cochrane Database and clinicaltrials.gov from inception to March 30, 2024. References of eligible studies were screened and experts in the field contacted for additional articles. STUDY ELIGIBILITY CRITERIA: All clinical studies, regardless of design, publication status and language. PARTICIPANTS: Adult patients with uncomplicated SAB. INTERVENTIONS: Long (>14 days; >18 days; 11-16 days) vs. short (≤14 days; 10-18 days; 6-10 days, respectively) DOT with the DOT being defined as the first until the last day of antibiotic therapy. ASSESSMENT OF RISK OF BIAS: Risk of bias was assessed using the ROBINS-I-tool. METHODS OF DATA SYNTHESIS: The primary outcome was 90-day all-cause mortality. Only studies presenting results of adjusted analyses for mortality were included. Data synthesis could not be performed. RESULTS: Eleven nonrandomized studies were identified that fulfilled the pre-defined inclusion criteria, of which three studies reported adjusted effect ratios. Only these were included in the final analysis. We did not find any RCT. Two studies with 1230 patients reported the primary endpoint 90-day all-cause mortality. Neither found a statistically significant superiority for longer (>14 days; 11-16 days) or shorter DOT (≤14 days; 6-10 days, respectively) for patients with uncomplicated SAB. Two studies investigated the secondary endpoint 30-day all-cause mortality (>18 days; 11-16 days vs. 10-18 days; 6-10 days, respect
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Journal articleHuybrechts I, Chimera B, Hanley-Cook GT, et al., 2024,
Food biodiversity and gastrointestinal cancer risk in nine European countries: analysis within a prospective cohort study
, European Journal of Cancer, Vol: 210, ISSN: 0959-8049BackgroundFood biodiversity in human diets has potential co-benefits for both public health and sustainable food systems. However, current evidence on the potential relationship between food biodiversity and cancer risk, and particularly gastrointestinal cancers typically related to diet, remains limited. This study evaluated how dietary species richness (DSR) was associated with gastrointestinal cancer risk in a pan-European population.MethodsAssociations between DSR and subsequent gastrointestinal cancer risk were examined among 450,111 adults enrolled in the European Prospective Investigation into Cancer and Nutrition cohort (EPIC, initiated in 1992), free of cancer at baseline. Usual dietary intakes were assessed at recruitment with country-specific dietary questionnaires. DSR of an individual’s yearly diet was calculated based on the absolute number of unique biological species in each food and drink item. Associations between DSR and cancer risk were assessed by multivariable Cox proportional hazards regression models.FindingsDuring a median follow-up time of 14.1 years (SD=3.9), 10,705 participants were diagnosed with gastrointestinal cancer. Hazard ratios (HRs) and 95 % confidence intervals (CIs) comparing overall gastrointestinal cancer risk in the highest versus lowest quintiles of DSR indicated inverse associations in multivariable-adjusted models [HR (95 % CI): 0.77 (0.69–0.87); P-value < 0·0001] (Table 2). Specifically, inverse associations were observed between DSR and oesophageal squamous cell carcinoma, proximal colon, colorectal, and liver cancer risk (p-trend<0.05 for all cancer types).InterpretationGreater food biodiversity in the diet may lower the risk of certain gastrointestinal cancers. Further research is needed to replicate these novel findings and to understand potential mechanisms.
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Journal articlePerez Guzman PN, Longa Chanda S, Schaap A, et al., 2024,
Pandemic burden in low-income settings and impact of limited and delayed interventions: a granular modelling analysis of COVID-19 in Kabwe, Zambia
, International Journal of Infectious Diseases, Vol: 147, ISSN: 1201-9712ObjectivesPandemic response in low-income countries (LICs) or settings often suffers from scarce epidemic surveillance and constrained mitigation capacity. The drivers of pandemic burden in such settings, and the impact of limited and delayed interventions remain poorly understood.MethodsWe analysed COVID-19 seroprevalence and all-cause excess deaths data from the peri-urban district of Kabwe, Zambia between March 2020 and September 2021 with a novel mathematical model. Data encompassed three consecutive waves caused by the wild-type, Beta and Delta variants.ResultsAcross all three waves, we estimated a high cumulative attack rate, with 78% (95% credible interval [CrI] 71-85) of the population infected, and a high all-cause excess mortality, at 402 (95% CrI 277-473) deaths per 100,000 people. Ambitiously improving health care to a capacity similar to that in high-income settings could have averted up to 46% (95% CrI 41-53) of accrued excess deaths, if implemented from June 2020 onward. An early and accelerated vaccination rollout could have achieved the highest reductions in deaths. Had vaccination started as in some high-income settings in December 2020 and with the same daily capacity (doses per 100 population), up to 68% (95% CrI 64-71) of accrued excess deaths could have been averted. Slower rollouts would have still averted 62% (95% CrI 58-68), 54% (95% CrI 49-61) or 26% (95% CrI 20-38) of excess deaths if matching the average vaccination capacity of upper-middle-, lower-middle- or LICs, respectively.ConclusionsRobust quantitative analyses of pandemic data are of pressing need to inform future global pandemic preparedness commitments.
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Journal articleArinaminpathy N, Reed C, Biggerstaff M, et al., 2024,
Estimating community-wide indirect effects of influenza vaccination: triangulation using mathematical models and bias analysis.
, Am J EpidemiolUnderstanding 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 articleDoohan P, Jorgensen D, Naidoo T, et al., 2024,
Lassa fever outbreaks, mathematical models, and disease parameters: a systematic review and meta-analysis
, The Lancet Global Health, ISSN: 2214-109X -
Journal articleValente de Almeida S, Hauck K, Njenga S, et al., 2024,
Value for money of medicine sampling and quality testing: evidence from Indonesia
, BMJ Global Health -
Journal articleGifford H, Rhodes J, Farrer RA, 2024,
The diverse genomes of Candida auris
, The Lancet Microbe, Vol: 5 -
Journal articleKucharski A, Cori A, 2024,
Inference of epidemic dynamics in the COVID-19 era and beyond
, Epidemics: the journal of infectious disease dynamics, Vol: 48, ISSN: 1755-4365The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats and supporting decision making in real-time. Motivated by the unprecedented volume and breadth of data generated during the pandemic, we review modern opportunities for analysis to address questions that emerge during a major modern epidemic. Following the broad chronology of insights required — from understanding initial dynamics to retrospective evaluation of interventions, we describe the theoretical foundations of each approach and the underlying intuition. Through a series of case studies, we illustrate real life applications, and discuss implications for future work.
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