Design and Estimation for the Population Prevalence of Infectious Diseases

AbstractUnderstanding the prevalence of infections in the population of interest is critical for making data-driven public health responses to infectious disease outbreaks. Accurate prevalence estimates, however, can be difficult to calculate due to a combination of low population prevalence, imperfect diagnostic tests, and limited testing resources. In addition, strategies based on convenience samples that target only symptomatic or high-risk individuals will yield biased estimates of the population prevalence. We present Bayesian multilevel regression and poststratification models that incorporate probability sampling designs, the sensitivity and specificity of a diagnostic test, and specimen pooling to obtain unbiased prevalence estimates. These models easily incorporate all available prior information and can yield reasonable inferences even with very low base rates and limited testing resources. We examine the performance of these models with an extensive numerical study that varies the sampling design, sample size, true prevalence, and pool size. We also demonstrate the relative robustness of the models to key prior distribution assumptions via sensitivity analyses.

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Infectious disease outbreaks in the African region: overview of events reported to the World Health Organization in 2018

Epidemiology and Infection ◽

10.1017/s0950268819001912 ◽

2019 ◽

Vol 147 ◽

Cited By ~ 6

Author(s):

F. Mboussou ◽

P. Ndumbi ◽

R. Ngom ◽

Z. Kassamali ◽

O. Ogundiran ◽

...

Keyword(s):

Infectious Disease ◽

Disease Onset ◽

Disease Outbreaks ◽

Case Fatality ◽

World Health ◽

African Region ◽

Regional Office ◽

Member States ◽

Infectious Disease Outbreaks ◽

The World

Abstract The WHO African region is characterised by the largest infectious disease burden in the world. We conducted a retrospective descriptive analysis using records of all infectious disease outbreaks formally reported to the WHO in 2018 by Member States of the African region. We analysed the spatio-temporal distribution, the notification delay as well as the morbidity and mortality associated with these outbreaks. In 2018, 96 new disease outbreaks were reported across 36 of the 47 Member States. The most commonly reported disease outbreak was cholera which accounted for 20.8% (n = 20) of all events, followed by measles (n = 11, 11.5%) and Yellow fever (n = 7, 7.3%). About a quarter of the outbreaks (n = 23) were reported following signals detected through media monitoring conducted at the WHO regional office for Africa. The median delay between the disease onset and WHO notification was 16 days (range: 0–184). A total of 107 167 people were directly affected including 1221 deaths (mean case fatality ratio (CFR): 1.14% (95% confidence interval (CI) 1.07%–1.20%)). The highest CFR was observed for diseases targeted for eradication or elimination: 3.45% (95% CI 0.89%–10.45%). The African region remains prone to outbreaks of infectious diseases. It is therefore critical that Member States improve their capacities to rapidly detect, report and respond to public health events.

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A Bayesian approach to estimating the population prevalence of mood and anxiety disorders using multiple measures

Epidemiology and Psychiatric Sciences ◽

10.1017/s2045796020001080 ◽

2021 ◽

Vol 30 ◽

Author(s):

Jordan Edwards ◽

A. Demetri Pananos ◽

Amardeep Thind ◽

Saverio Stranges ◽

Maria Chiu ◽

...

Keyword(s):

Monte Carlo ◽

Anxiety Disorders ◽

Administrative Data ◽

Population Based ◽

Accurate Estimate ◽

Service Planning ◽

Prevalence Estimate ◽

Population Prevalence ◽

Mood And Anxiety Disorders ◽

Prevalence Estimates

Abstract Aims There is currently no universally accepted measure for population-based surveillance of mood and anxiety disorders. As such, the use of multiple linked measures could provide a more accurate estimate of population prevalence. Our primary objective was to apply Bayesian methods to two commonly employed population measures of mood and anxiety disorders to make inferences regarding the population prevalence and measurement properties of a combined measure. Methods We used data from the 2012 Canadian Community Health Survey – Mental Health linked to health administrative databases in Ontario, Canada. Structured interview diagnoses were obtained from the survey, and health administrative diagnoses were identified using a standardised algorithm. These two prevalence estimates, in addition to data on the concordance between these measures and prior estimates of their psychometric properties, were used to inform our combined estimate. The marginal posterior densities of all parameters were estimated using Hamiltonian Monte Carlo (HMC), a Markov Chain Monte Carlo technique. Summaries of posterior distributions, including the means and 95% equally tailed posterior credible intervals, were used for interpretation of the results. Results The combined prevalence mean was 8.6%, with a credible interval of 6.8–10.6%. This combined estimate sits between Bayesian-derived prevalence estimates from administrative data-derived diagnoses (mean = 7.4%) and the survey-derived diagnoses (mean = 13.9%). The results of our sensitivity analysis suggest that varying the specificity of the survey-derived measure has an appreciable impact on the combined posterior prevalence estimate. Our combined posterior prevalence estimate remained stable when varying other prior information. We detected no problematic HMC behaviour, and our posterior predictive checks suggest that our model can reliably recreate our data. Conclusions Accurate population-based estimates of disease are the cornerstone of health service planning and resource allocation. As a greater number of linked population data sources become available, so too does the opportunity for researchers to fully capitalise on the data. The true population prevalence of mood and anxiety disorders may reside between estimates obtained from survey data and health administrative data. We have demonstrated how the use of Bayesian approaches may provide a more informed and accurate estimate of mood and anxiety disorders in the population. This work provides a blueprint for future population-based estimates of disease using linked health data.

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Antimicrobial Activity of Chitosan Oligosaccharides with Special Attention to Antiparasitic Potential

Marine Drugs ◽

10.3390/md19020110 ◽

2021 ◽

Vol 19 (2) ◽

pp. 110

Author(s):

Nayara Sousa da Silva ◽

Nathália Kelly Araújo ◽

Alessandra Daniele-Silva ◽

Johny Wysllas de Freitas Oliveira ◽

Júlia Maria de Medeiros ◽

...

Keyword(s):

Disease Outbreaks ◽

Biological Properties ◽

Enzymatic Production ◽

Infectious Disease Outbreaks ◽

Chitosan Oligosaccharides ◽

Lower Viscosity ◽

Biomedical Industry ◽

Parasite Aggregation ◽

Hydrolysis Of

The global rise of infectious disease outbreaks and the progression of microbial resistance reinforce the importance of researching new biomolecules. Obtained from the hydrolysis of chitosan, chitooligosaccharides (COSs) have demonstrated several biological properties, including antimicrobial, and greater advantage over chitosan due to their higher solubility and lower viscosity. Despite the evidence of the biotechnological potential of COSs, their effects on trypanosomatids are still scarce. The objectives of this study were the enzymatic production, characterization, and in vitro evaluation of the cytotoxic, antibacterial, antifungal, and antiparasitic effects of COSs. NMR and mass spectrometry analyses indicated the presence of a mixture with 81% deacetylated COS and acetylated hexamers. COSs demonstrated no evidence of cytotoxicity upon 2 mg/mL. In addition, COSs showed interesting activity against bacteria and yeasts and a time-dependent parasitic inhibition. Scanning electron microscopy images indicated a parasite aggregation ability of COSs. Thus, the broad biological effect of COSs makes them a promising molecule for the biomedical industry.

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549. Clinical Characteristics and Outcomes of Patients with COVID-19 treated with Convalescent Plasma in Miami, Florida

Open Forum Infectious Diseases ◽

10.1093/ofid/ofaa439.743 ◽

2020 ◽

Vol 7 (Supplement_1) ◽

pp. S340-S341

Author(s):

Shweta Anjan ◽

Dimitra Skiada ◽

Miriam Andrea Duque Cuartas ◽

Douglas Salguero ◽

David P Serota ◽

...

Keyword(s):

Treatment Options ◽

Disease Outbreaks ◽

Nasopharyngeal Swab ◽

Rt Pcr ◽

Health Crisis ◽

Infectious Disease Outbreaks ◽

Reactive Protein ◽

High Acuity ◽

Us Fda ◽

Oxygen Requirements

Abstract Background The Coronavirus disease of 2019 (COVID-19) global health crisis caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in unprecedented mortality, impacted society, and strained healthcare systems, yet sufficient data regarding treatment options are lacking. Convalescent plasma, used since 1895 for infectious disease outbreaks, offers promise as a treatment option for COVID-19. Methods This is a retrospective study of patients diagnosed by a nasopharyngeal swab SARS-CoV-2 reverse transcriptase–polymerase chain reaction (RT-PCR), who received convalescent plasma between April to June 2020 at two large hospitals in Miami, Florida, as part of the US FDA Expanded Access Program for COVID-19 convalescent plasma (CCP). Results A total of 23 patients received CCP, 13 (57%) had severe COVID-19 disease, while 8 (35%) had critical or critical with multiorgan dysfunction. Median time of follow up was 26 (range, 7–79) days. Overall, 11 (48%) survived to discharge, 6 (26%) died, while 6 (26%) are currently hospitalized. All deaths reported were due to septic shock from secondary infections. 15 (65%) showed improvement in oxygen requirements 7 days post CCP transfusion. Measured inflammatory markers, c-reactive protein, lactate dehydrogenase, ferritin and d-dimer improved 7 days post transfusion in 13 (57%) patients. No adverse events due to the transfusion were reported. 10 (43.4%) patients had a negative SARS-CoV-2 RT-PCR at a median of 14.5 (range, 4–31) days after receiving convalescent plasma. Conclusion Administration of convalescent plasma was found to be safe, with favorable outcomes in this small cohort of relatively high acuity patients. Larger studies including control arms are needed to establish the efficacy of convalescent plasma on clinical and virologic outcomes for patients with COVID-19. Table Disclosures All Authors: No reported disclosures

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Digital proximity tracing on empirical contact networks for pandemic control

Nature Communications ◽

10.1038/s41467-021-21809-w ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

G. Cencetti ◽

G. Santin ◽

A. Longa ◽

E. Pigani ◽

A. Barrat ◽

...

Keyword(s):

Large Scale ◽

Disease Outbreaks ◽

Physical Distance ◽

Contact Tracing ◽

Reproductive Number ◽

Modeling Framework ◽

Infectious Disease Outbreaks ◽

Contact Patterns ◽

Contact Data ◽

The Impact

AbstractDigital contact tracing is a relevant tool to control infectious disease outbreaks, including the COVID-19 epidemic. Early work evaluating digital contact tracing omitted important features and heterogeneities of real-world contact patterns influencing contagion dynamics. We fill this gap with a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread in real environments. We find that restrictive policies are more effective in containing the epidemic but come at the cost of unnecessary large-scale quarantines. Policy evaluation through their efficiency and cost results in optimized solutions which only consider contacts longer than 15–20 minutes and closer than 2–3 meters to be at risk. Our results show that isolation and tracing can help control re-emerging outbreaks when some conditions are met: (i) a reduction of the reproductive number through masks and physical distance; (ii) a low-delay isolation of infected individuals; (iii) a high compliance. Finally, we observe the inefficacy of a less privacy-preserving tracing involving second order contacts. Our results may inform digital contact tracing efforts currently being implemented across several countries worldwide.

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Threshold Effects of Infectious Disease Outbreaks on Livestock Prices: Cases of African Swine Fever and Avian Influenza in South Korea

Applied Sciences ◽

10.3390/app11115114 ◽

2021 ◽

Vol 11 (11) ◽

pp. 5114

Author(s):

Hyung-Chul Rah ◽

Hyeon-Woong Kim ◽

Aziz Nasridinov ◽

Wan-Sup Cho ◽

Seo-Hwa Choi ◽

...

Keyword(s):

Avian Influenza ◽

Infectious Diseases ◽

Disease Outbreaks ◽

African Swine Fever ◽

Threshold Effect ◽

Impact Response ◽

Threshold Effects ◽

Infectious Disease Outbreaks ◽

Retail Prices ◽

The Impact

In this paper we demonstrate the threshold effects of infectious diseases on livestock prices. Daily retail prices of pork and chicken were used as structured data; news and SNS mentions of African Swine Fever (ASF) and Avian Influenza (AI) were used as unstructured data. Models were tested for the threshold effects of disease-related news and SNS frequencies, specifically those related to ASF and AI, on the retail prices of pork and chicken, respectively. The effects were found to exist, and the values of ASF-related news on pork prices were estimated to be −9 and 8, indicating that the threshold autoregressive (TAR) model can be divided into three regimes. The coefficients of the ASF-related SNS frequencies on pork prices were 1.1666, 0.2663 and −0.1035 for regimes 1, 2 and 3, respectively, suggesting that pork prices increased by 1.1666 Korean won in regime 1 when ASF-related SNS frequencies increased. To promote pork consumption by SNS posts, the required SNS frequencies were estimated to have impacts as great as one standard deviation in the pork price. These values were 247.057, 1309.158 and 2817.266 for regimes 1, 2 and 3, respectively. The impact response periods for pork prices were estimated to last 48, 6, and 8 days for regimes 1, 2 and 3, respectively. When the prediction accuracies of the TAR and autoregressive (AR) models with regard to pork prices were compared for the root mean square error, the prediction accuracy of the TAR model was found to be slightly better than that of the AR. When the threshold effect of AI-related news on chicken prices was tested, a linear relationship appeared without a threshold effect. These findings suggest that when infectious diseases such as ASF occur for the first time, the impact on livestock prices is significant, as indicated by the threshold effect and the long impact response period. Our findings also suggest that the impact on livestock prices is not remarkable when infectious diseases occur multiple times, as in the case of AI. To date, this study is the first to suggest the use of SNS to promote meat consumption.

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Statistical methods for the prospective detection of infectious disease outbreaks: a review

Journal of the Royal Statistical Society Series A (Statistics in Society) ◽

10.1111/j.1467-985x.2011.00714.x ◽

2011 ◽

Vol 175 (1) ◽

pp. 49-82 ◽

Cited By ~ 150

Author(s):

Steffen Unkel ◽

C. Paddy Farrington ◽

Paul H. Garthwaite ◽

Chris Robertson ◽

Nick Andrews

Keyword(s):

Infectious Disease ◽

Statistical Methods ◽

Disease Outbreaks ◽

Infectious Disease Outbreaks

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Information Communication Technologies (ICTs), Crisis Communication Principles and the COVID-19 Response in South Korea

Journal of Creative Communications ◽

10.1177/0973258620981170 ◽

2021 ◽

pp. 097325862098117

Author(s):

Hye-Jin Paek ◽

Thomas Hove

Keyword(s):

Crisis Communication ◽

Large Scale ◽

Communication Strategies ◽

Disease Outbreaks ◽

Information Communication ◽

Information And Communications Technologies ◽

The South ◽

South Korean ◽

Infectious Disease Outbreaks ◽

Control Programmes

This case study highlights several communication insights that have emerged from the South Korean national response to COVID-19. In particular, it focuses on how innovative disease control programmes and information and communications technologies (ICT) have been used in conjunction with appropriate message strategies. The South Korean government used ICTs in a variety of ways to enhance crisis communication, coordinate large-scale public health efforts and supply chains, and facilitate widespread adoption of preventive measures such as social distancing and mask wearing. The response and communication strategies were based on principles established by research in social sciences and recommended for pandemic response, including social marketing, crisis communication, and normative influence. South Korea’s COVID-19 response and communication strategies can provide useful insights for national efforts to manage COVID-19 and other possible future infectious disease outbreaks.

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