Infectious Diseases

An urgent need of implementing national surveillance systems for timely detection and reporting of emerging antimicrobial resistance (AMR) was recently advocated by the World Health Organization (WHO).

However, public information on existing national early warning systems (EWSs) is often incomplete, and a comprehensive overview on this topic is currently lacking.

Objective of this study: To map the availability of EWSs for emerging AMR in high-income countries and describe their main characteristics.

Methods: A systematic review was performed on bibliographic databases, and a targeted search was conducted on national websites. Any article, report or webpage describing national EWSs in high-income countries was eligible for inclusion. EWSs were identified considering the emerging AMR reporting WHO framework. Results: We identified seven national EWSs in 72 high-income countries: two (Australia, Japan) in the East Asia and Pacific Region, three (France, Sweden, United Kingdom) in Europe and Central Asia, and two (United States, Canada) in North America.

The systems were established quite recently; in most cases they covered both community and hospital settings, but their main characteristics varied widely across countries in terms of organization and microorganisms under surveillance, with also different definitions of emerging AMR and alert functioning. A formal system assessment was available only in Australia.

Conclusions: A broader implementation and investment of national surveillance systems that allow early detection of emerging AMR is still needed to establish EWSs in countries and regions lacking such capabilities.

More standardized data collection and reporting are also advisable to improve cooperation on a global scale. Further research is required to provide an in-depth analysis of EWSs, as this study is limited to publicly available data on HICs.

The Covid catastrophe brought with it "important learnings, and those learnings were drawn upon", said Gavi chief Sania Nishtar."Those learnings cascaded into specific instruments that are now coming into play" against mpox, the former Pakistani health minister told AFP in an interview this week.

read more at https://health.economictimes.indiatimes.com/news/industry/world-better-positioned-against-mpox-than-for-covid-vaccine-alliance/113478217?utm_source=Mailer&utm_medium=newsletter&utm_campaign=ethealth_news_2024-09-20&dt=2024-09-20&em=bi5uaWhhbGFuaUBwbHVzOTEuaW4=

A team of researchers analyzed the genomes of more than 2,500 modern humans from 26 worldwide populations, to better understand how humans have adapted to historical coronavirus outbreaks.

The team used computational methods to uncover genetic traces of adaptation to coronaviruses, the family of viruses responsible for three major outbreaks in the last 20 years, including the ongoing COVID-19 pandemic.

Traces of the outbreak are evident in the genetic makeup of people from that area, they’ve found.

A coronavirus epidemic broke out in the East Asia region more than 20,000 years ago, as per their findings.

The discovery of a coronavirus outbreak from 20,000 years ago is "like finding fossilized dinosaur footprints instead of finding fossilized bones directly.

The work shows that over the course of the epidemic, selection favored certain variants of human genes involved in the virus-cell interactions that could have led to a less severe disease. Studying the “tracks” left by ancient viruses can help researchers better understand how the genomes of different human populations adapted to viruses that have emerged as important drivers of human evolution.

The study’s authors say their research could help identify viruses that have caused epidemics in the distant past and may do so in the future. Studies like theirs help researchers compile a list of potentially dangerous viruses and then develop diagnostics, vaccines, and drugs for the event of their return.

read the paper at https://www.cell.com/current-biology/fulltext/S0960-9822(21)00794-6

more at https://www.futurity.org/coronavirus-epidemic-viruses-2597742/

Background: In March 2020, South Africa implemented strict nonpharmaceutical interventions (NPIs) to contain the spread of COVID-19. Over the subsequent 5 months, NPI policies were eased in stages according to a national strategy.

COVID-19 spread throughout the country heterogeneously; the case numbers peaked from July to August. A second COVID-19 wave began in late 2020. Data on the impact of NPI policies on social and economic well-being and access to health care are limited.

Objective: We aimed to determine how rural residents in three South African provinces changed their behaviors during the first COVID-19 epidemic wave.

Methods: The South African Population Research Infrastructure Network nodes in the Mpumalanga (Agincourt), KwaZulu-Natal, (Africa Health Research Institute) and Limpopo (Dikgale-Mamabolo-Mothiba) provinces conducted up to 14 rounds of longitudinal telephone surveys among randomly sampled households from rural and periurban surveillance populations every 2-3 weeks. Interviews included questions on the following topics: COVID-19–related knowledge and behaviors, the health and economic impacts of NPIs, and mental health. We analyzed how responses varied based on NPI stringency and household sociodemographics.

Conclusions: South Africans complied with stringent, COVID-19–related NPIs despite the threat of substantial social, economic, and health repercussions. Government-supported social welfare programs appeared to buffer interruptions in income and health care access during local outbreaks. Epidemic control policies must be balanced against the broader well-being of people in resource-limited settings and designed with parallel support systems when such policies threaten peoples’ income and access to basic services.

In March 2022, a concerning rise in cases of unexplained paediatric hepatitis was reported in multiple countries.

Cases were defined as acute hepatitis with serum transaminases >500 U/L (aspartate transaminase (AST) or alanine transaminase (ALT)) in children aged 16 or under.

In this study the authors explore a simple federated data analytics method to search for evidence of unreported cases using routinely held data.

They conducted a pragmatic survey to analyse changes in the proportion of hospitalised children with elevated AST or ALT over time. In addition, they studied the feasibility of using routinely collected clinical laboratory results to detect or follow up an outbreak of an infectious disease.

Results: Our approach of sharing a simple software tool for local use enabled rapid, federated data analysis. 34 hospitals in the UK, the Netherlands, Ireland and Curaçao were asked to contribute summary data, and 30 (88%) submitted their data.

For all locations combined, the rate of elevated AST or ALT measurements in the period of interest was not elevated (Z-score -0.46; p 0.64). Results from individual regions were discordant, with a higher rate of elevated AST or ALT values in the Netherlands (Z-score 4.48; p<0.0001), driven by results from a single centre in Utrecht. We did not observe any clear indication of changes in primary care activity or test results in the same period.

Conclusions: Hospital laboratories collect large amounts of data on a daily basis that can potentially be of use for disease surveillance, but these are currently not optimally utilised.

Federated analytics using non-disclosive, summary-level laboratory data sharing was successful, safe and efficient. The approach holds potential as a tool for pandemic surveillance in future outbreaks.

Our findings do not indicate the presence of a broader outbreak of mild hepatitis cases among young children, although there was an increase in elevated AST or ALT values locally in the Netherlands.

read the original unedited research work at https://publichealth.jmir.org/2024/1/e55376/

During the peak of the winter 2020-2021 surge, the number of weekly reported COVID-19 outbreaks in Washington State was 231; the majority occurred in high-priority settings such as workplaces, community settings, and schools.

The Washington State Department of Health used automated address matching to identify clusters at health care facilities. No other systematic, statewide outbreak detection methods were in place.

This was a gap given the high volume of cases, which delayed investigations and decreased data completeness, potentially leading to undetected outbreaks.

We initiated statewide cluster detection using SaTScan, implementing a space-time permutation model to identify COVID-19 clusters for investigation.

Objective: To improve outbreak detection, the Washington State Department of Health initiated a systematic cluster detection model to identify timely and actionable COVID-19 clusters for local health jurisdiction (LHJ) investigation and resource prioritization.

This report details the model’s implementation and the assessment of the tool’s effectiveness.

Regardless of population or incidence, the model identified reasonably sized, timely clusters statewide, meeting the objective.

Among some high-priority settings subject to public health interventions throughout the pandemic, such as schools and community settings, the model identified clusters that were matched to reported outbreaks.

In workplaces, another high-priority setting, results suggest the model might be able to identify outbreaks sooner than existing outbreak detection methods.

read the research paper at https://publichealth.jmir.org/2024/1/e49871/

The pandemics of major infectious diseases often cause public health, economic, and social problems. Virtual reality (VR) and augmented reality (AR), as two novel technologies, have been used in many fields for emergency management of disasters. The objective of this paper was to review VR and AR applications in the emergency management of infectious outbreaks with an emphasis on the COVID-19 outbreak.

It appears that VR and AR technologies can play a positive role during infectious disease outbreaks.

VR and AR have been widely used in the prevention and response phases of emergency management during infectious disease pandemics, such as SARS and Ebola pandemics, especially for educating and training purposes for the public.

During the COVID-19 outbreak, these technologies have the potential to be used in various fields, including

1) clinical context (e.g., telehealth, drug discovery, patient assessment, mental health management),

2) entertainment (e.g., video call, meditation, gaming),

3) business and industry (e.g., holding meetings and conferences, marketing), and

4) education (e.g., in schools and universities, for healthcare providers, and VR-based content for improving public health).

These technologies can be used in the above-mentioned fields by providing their different features for facilitating the challenges of COVID-19.

However, to respond to COVID-19, all applications of VR and AR should be considered as a supportive approach alongside other information technologies.

We believe that VR and AR have a substantial potential to impact the emergency management of COVID-19 or any infectious disease pandemics; however, these potentials need to be studied in a more robust manner.

read the paper ta https://www.sciencedirect.com/science/article/pii/S2352914821000691

The national severe acute respiratory illness (SARI) surveillance system in Yemen was established in 2010 to monitor SARI occurrence in humans and provide a foundation for detecting SARI outbreaks.

This study aimed to examine the level of usefulness and the performance of the SARI surveillance system in Yemen.

Methods: The updated Centers for Disease Control and Prevention guidelines were used for the purposes of our evaluation. Related documents and reports were reviewed. Data were collected from 4 central-level managers and stakeholders and from 10 focal points at 4 sentinel sites by using a semistructured questionnaire.

For each attribute, percent scores were calculated and ranked as follows: very poor (≤20%), poor (20%-40%), average (40%-60%), good (60%-80%), and excellent (>80%).

The surveillance systems at sentinel sites are tools for the early detection of disease, the monitoring of trends in the burden of diseases, and the generation of recommendations for the prevention and control of diseases. The evaluation of surveillance systems helps decision makers to set priorities for future planning, resource allocation, and future interventions for preventing the spread of diseases.

Results: As rated by the evaluators, the SARI surveillance system achieved its objectives.

The system’s flexibility (percent score: 86%) and acceptability (percent score: 82%) were rated as “excellent,” and simplicity (percent score: 74%) and stability (percent score: 75%) were rated as “good.”

The percent score for timeliness was 23% in 2018, which indicated poor timeliness. The overall data quality percent score of the SARI system was 98.5%.

Despite its many strengths, the SARI system has some weaknesses. For example, it depends on irregular external financial support.

The SARI surveillance system was useful in estimating morbidity and mortality, monitoring the trends of the disease, and promoting research for informing prevention and control measures.

The overall performance of the SARI surveillance system was good.

We recommend expanding the system by promoting private health facilities’ (eg, private hospitals and private health centers) engagement in SARI surveillance, establishing an electronic database at central and peripheral sites, and providing the National Central Public Health Laboratory with the reagents needed for disease confirmation.

Pandemic SEIR and SEIRV modelling software and infrastructure for the Corona SARS-COV-2 COVID-19 disease with data from Johns-Hopkins-University CSSE, Robert Koch-Institute and vaccination data from Our World In Data.

In a clinical trial, diagnostic testing of suspected cases e.g RT-PCR for severe acute respiratory SARS-CoV-2, can be triggered by the presence of any COVID-19 associated symptom.

A household survey in the United Kingdom (UK) showed that fever, cough, anosmia, and ageusia were present on the day of testing in only 60% of symptomatic, RT-PCR positive individuals, implying that other less specific signs/symptoms associated with COVID-19 occur in a substantial number of patients.

The signs/symptoms associated with COVID-19 are extensive and overlap with those of other common viral infections.

Diagnostic work-up following any COVID-19 associated symptom will lead to extensive testing, potentially overwhelming laboratory capacity whilst primarily yielding negative results. We aimed to identify optimal symptom combinations to capture most cases using fewer tests with implications for COVID-19 vaccine developers across different resource settings and public health.

Methods

UK and US users of the COVID-19 Symptom Study app who reported new-onset symptoms and an RT-PCR test within seven days of symptom onset were included. Sensitivity, specificity, and number of RT-PCR tests needed to identify one case (test per case [TPC]) were calculated for different symptom combinations.

A multi-objective evolutionary algorithm was applied to generate combinations with optimal trade-offs between sensitivity and specificity.

Interpretation
We confirmed the significance of COVID-19 specific symptoms for triggering RT-PCR and identified additional symptom combinations with optimal trade-offs between sensitivity and specificity that maximize case capture given different resource settings.

read this paper at https://www.journalofinfection.com/article/S0163-4453(21)00079-7/fulltext