Major Outbreaks Research Articles

The outcome of COVID‐19 patients in the intensive care unit in Sudan: A cross‐sectional study

A major outbreak of coronavirus spread all over the world and gave rise to high mortality rate and high admission rate to intensive care unit (ICU). This cohort study aims to assess the outcome of COVID-19 patients in ICU and to investigate the factors associated with mortality. This is a multicentered retrospective cohort study that was conducted among confirmed cases of COVID-19 patients, who were admitted to ICU in Sudan during March 2021. The data collection was done manually from the medical records of patients. Mortality rate and association and prediction of factors associated with mortality were obtained using Statistical Package for the Social Sciences software (SPSS) version 22. The mortality rate among patients in this study was 70%. Using the chi-square test we found that age, needing intubation, developing Systemic inflammatory response syndrome, neurological complications, hematological complications, and cardiac complications have a significant association with the outcome. Majority of COVID-19 patients who were admitted to the ICU died. 55.8% of patients developed at least one complication during their stay in ICU. The age, the need for intubation, and developing of systematic inflammatory response syndrome (SIRS) are the factors that predict the mortality.

Comparing the incubation period, serial interval, and infectiousness profile between SARS-CoV-2 Omicron and Delta variants.

In January 2022, the SARS-CoV-2 Omicron variant initiated major outbreaks and dominated the transmissions in Hong Kong, displacing an earlier outbreak seeded by the Delta variants. To provide insight into the transmission potential of the emerging variants, we aimed to compare the epidemiological characteristics of the Omicron and Delta variants. We analysed the line-list clinical and contact tracing data of the SARS-CoV-2 confirmed cases in Hong Kong. Transmission pairs were constructed based on the individual contact history. We fitted bias-controlled models to the data to estimate the serial interval, incubation period and infectiousness profile of the two variants. Viral load data were extracted and fitted to the random effect models to investigate the potential risk modifiers for the clinical viral shedding course. Totally 14401 confirmed cases were reported between January 1 and February 15, 2022. The estimated mean serial interval (4.4 days vs 5.8 days) and incubation period (3.4 days vs 3.8 days) were shorter for the Omicron than the Delta variants. A larger proportion of pre-symptomatic transmission was observed for the Omicron (62%) compared to the Delta variants (48%). The Omicron cases had higher mean viral load over an infection course than the Delta cases, with the elder cases appearing more infectious than the younger cases for both variants. The epidemiological features of Omicron variant were likely an obstacle to contact tracing measures, imposed as a major intervention in settings like Hong Kong. Continuously monitoring the epidemiological feature for any emerging SARS-CoV-2 variants in the future is needed to assist officials in planning measures for COVID-19 control. This article is protected by copyright. All rights reserved.

Modeling cryptosporidiosis in humans and cattle: Deterministic and stochastic approaches

Cryptosporidiosis is a zoonotic disease caused by Cryptosporidium. The disease poses a public and veterinary health problem worldwide. A deterministic model and its corresponding continuous time Markov chain (CTMC) stochastic model are developed and analyzed to investigate cryptosporidiosis transmission dynamics in humans and cattle. The basic reproduction number R0 for the deterministic model and stochastic threshold for the CTMC stochastic model are computed by the next generation matrix method and multitype branching process, respectively. The normalized forward sensitivity index method is used to determine the sensitivity index for each parameter in R0. Per capita birth rate of cattle, the rate of cattle to acquire cryptosporidiosis infection from the environment and the rate at which infected cattle shed Cryptosporidium oocysts in the environment play an important role in the persistence of the disease whereas Cryptosporidium oocysts natural death rate, cattle recovery rate and cattle natural death rate are most negative sensitive parameters in the dynamics of cryptosporidiosis. Numerical results for CTMC stochastic model show that the likelihood of cryptosporidiosis extinction is high when it arises from an infected human. However, there is a major outbreak if cryptosporidiosis emerges either from infected cattle or from Cryptosporidium oocysts in the environment or when it emerges from all three infectious compartments. Therefore to control the disease, control measures should focus on maintaining personal and cattle farm hygiene and decontaminating the environment to destroy Cryptosporidium oocysts.

Increased rhinovirus/enterovirus infections including Ev-D68 in the United States, a challenge for healthcare providers amidst influenza virus infection and the COVID-19 pandemic.

In March 2020, WHO declared SARS-CoV-2 a global pandemic and lockdowns were enforced in most of the United States. Self-protective measures and travel restrictions were implemented, and schools/universities initiated online learning. Consequently, the global incidence and hospitalization rates of seasonal respiratory infections decreased significantly up to early 2021. Despite the decrease in hospitalization rates due to respiratory illnesses other than Covid-19, hospitals and healthcare providers faced huge stressors regarding workload dueto the pandemic. Furthermore, higher vaccination rates across the United States decreased non pharmaceutical interventions (NPI) implementation increasing the risk of major seasonal viral outbreaks. This editorial discusses the increased Rhinovirus/Enterovirus infections in the United States, challenges faced by healthcare providers and provides recommendations to address the issue.

Estimation of the Effectiveness of a Tighter, Reinforced Quarantine for the Coronavirus Disease 2019 (COVID-19) Outbreak: Analysis of the Third Wave in South Korea.

It has been claimed that a tighter, reinforced quarantine strategy was advocated to reduce the transmission of coronavirus disease 2019 (COVID-19) during major outbreaks; however, there have been no prior quantitative studies examining the effectiveness and duration of such a reinforced quarantine. Consequently, the purpose of this research was to determine the impact of a "tighter, reinforced" quarantine during the third COVID-19 breakout wave in South Korea, which occurred between late 2020 and early 2021. The efficacy of the quarantine was determined by comparing the number of newly diagnosed COVID-19 patients between the "prediction model" and "actual observed data." Two prediction models were developed using the autoregressive integrated moving average (ARIMA; 1, 0, 0) model. The effect of a "tighter, reinforced" quarantine, which would show as an immediate drop in the number of new cases, predicted its efficacy by lowering the number of new cases by 20,400. In addition, the efficacy of the quarantine lasted up to more than three months. The findings of our investigation confirmed the beneficial influence of "tighter, controlled" quarantine laws during a widespread COVID-19 epidemic. During an epidemic, when the population has not yet developed immunity to respiratory viral diseases, our study may be evidence for implementing stricter quarantine restrictions in order to reduce the number of new cases.

Identification of West Nile virus RNA-dependent RNA polymerase non-nucleoside inhibitors by real-time high throughput fluorescence screening

West Nile virus (WNV) is a re-emergent mosquito-borne RNA virus that causes major outbreaks of encephalitis around the world. However, there is no therapeutic treatment to struggle against WNV, and the current treatment relies on alleviating symptoms. Therefore, due to the threat virus poses to animal and human health, there is an urgent need to come up with fast strategies to identify and assess effective antiviral compounds. A relevant target when developing drugs against RNA viruses is the viral RNA-dependent RNA polymerase (RdRp), responsible for the replication of the viral genome within a host cell. RdRps are key therapeutic targets based on their specificity for RNA and their essential role in the propagation of the infection. We have developed a fluorescence-based method to measure WNV RdRp activity in a fast and reliable real-time way. Interestingly, rilpivirine has shown in our assay inhibition of the WNV RdRp activity with an IC50 value of 3.3 μM and its antiviral activity was confirmed in cell cultures. Furthermore, this method has been extended to build up a high-throughput screening platform to identify WNV polymerase inhibitors. By screening a small chemical library, novel RdRp inhibitors 1–4 have been identified. When their antiviral activity was tested against WNV in cell culture, 4 exhibited an EC50 value of 2.5 μM and a selective index of 12.3. Thus, rilpivirine shows up as an interesting candidate for repurposing against flavivirus. Moreover, the here reported method allows the rapid identification of new WNV RdRp inhibitors.

Spatial and temporal distribution of emerging airborne viral infectious diseases outbreaks on a global scale.

This study aimed to explore the spatial and temporal characteristics of emerging airborne viral infectious diseases outbreaks worldwide. We conducted a systematic literature review on outbreaks of emerging airborne viral infectious diseases and calculated outbreak number and intensity at the country level. Fisher's exact test was used to compare the viral infectious diseases outbreaks in different income-level regions. To identify the major airborne viral infectious diseases outbreaks, we ranked and extracted the leading viral infectious diseases in outbreak number and intensity in each country by year. A total of 2505 outbreaks were reported from 1873 to 2021 across 2010 studies. There were 47 countries (47/130, 36.15%) with more frequent emerging airborne viral infectious disease outbreaks (more than nine outbreaks), and these countries mainly distributed in high-income regions (22/47 countries, 46.81%, p < 0.05), especially in Western Europe (14/47 countries, 29.79%, p < 0.05). The number of overall outbreaks was more in the United States and China than in other countries in different years. Outbreaks of measles and influenza are always frequent and intense. Highly pathogenic human coronaviruses infection caused short-term pandemics during which their outbreak number and intensity exceeded other viruses. Rift valley fever outbreaks in the human population are spreading outside of Africa through the flow of goods and travelers. Countries in high-income regions reported more emerging airborne viral infectious diseases outbreaks, especially in the Western European region, the United States, and China. It is urgent to strengthen collaborative surveillance of emerging airborne viruses, cross-border flow of goods and travelers, and ecological environment to avoid the spread of viral infectious diseases outbreaks worldwide. The online version contains supplementary material available at 10.1007/s10389-023-01850-3.

Association of Hematological and Biochemical Parameters with Serological Markers of Acute Dengue Infection during the 2022 Dengue Outbreak in Nepal.

Nepal faced a major dengue outbreak in 2022. The majority of hospitals and laboratories had limited resources for dengue confirmation and had to rely on rapid dengue diagnostic tests. The purpose of the study is to find the predictive hematological and biochemical parameters in each serological phase of dengue infection (NS1 and IgM) that may assist in dengue diagnosis, severity assessment, and patient management via the use of rapid serological tests. A laboratory-based cross-sectional study was conducted among dengue patients. Rapid antigen (NS1) and serological test (IgM/IgG) was performed to diagnose positive dengue cases. Furthermore, hematological and biochemical investigations were carried out and compared between NS1 and/or IgM-positive participants. A logistic regression analysis was used to identify the validity of the hematological and biochemical characteristics for dengue diagnosis as well as patient management. Receiver-operating characteristic (ROC) curve analysis was used to define the best cut-off, sensitivity, and specificity. Multiple logistic regression showed thrombocytopenia (ORA = 1.000; p = 0.006), leukopenia (ORA = 0.999; p < 0.001), glucose level (ORA = 1.028; p = 0.029), aspartate aminotransferase (ORA = 1.131; p = 0.001), and monocytosis (ORA = 2.332; p = 0.020) as significant parameters in the NS1-only positive group. Similarly, thrombocytopenia (ORA = 1.000; p = 0.001), glucose level (ORA = 1.037; p = 0.004), and aspartate aminotransferase (ORA = 1.141; p < 0.001) were significant in IgM-only positive patients. Moreover, thrombocytopenia (ORA = 1.000; p < 0.001), leukopenia (ORA = 0.999; p < 0.001), glucose (ORA = 1.031; p = 0.017), aspartate aminotransferase (ORA = 1.136; p < 0.001), and lymphopenia (ORA = 0.520; p = 0.067) were independent predictors in both NS1 + IgM positive groups. Platelets consistently demonstrated a higher area under the curve with increased sensitivity and specificity throughout all models, while aspartate aminotransferase (AUC = 0.811) and glucose (AUC = 0.712) demonstrated better results when single IgM positivity was observed. The total leukocyte count performed better when both NS1 + IgM were positive (AUC = 0.814). Hence, thrombocytopenia, elevated AST, high glucose level, leukopenia with monocytosis, and leukopenia with lymphopenia may predict dengue diagnosis and its severity during an active infection. Therefore, these laboratory parameters can be used to complement less sensitive rapid tests, improve dengue diagnosis, and help with proper patient management.

Real-time estimation and forecasting of COVID-19 cases and hospitalizations in Wisconsin HERC regions for public health decision making processes

BackgroundThe spread of the COVID-19 (SARS-CoV-2) and the surging number of cases across the United States have resulted in full hospitals and exhausted health care workers. Limited availability and questionable reliability of the data make outbreak prediction and resource planning difficult. Any estimates or forecasts are subject to high uncertainty and low accuracy to measure such components. The aim of this study is to apply, automate, and assess a Bayesian time series model for the real-time estimation and forecasting of COVID-19 cases and number of hospitalizations in Wisconsin healthcare emergency readiness coalition (HERC) regions.MethodsThis study makes use of the publicly available Wisconsin COVID-19 historical data by county. Cases and effective time-varying reproduction number R_t by the HERC region over time are estimated using Bayesian latent variable models. Hospitalizations are estimated by the HERC region over time using a Bayesian regression model. Cases, effective Rt, and hospitalizations are forecasted over a 1-day, 3-day, and 7-day time horizon using the last 28 days of data, and the 20%, 50%, and 90% Bayesian credible intervals of the forecasts are calculated. The frequentist coverage probability is compared to the Bayesian credible level to evaluate performance.ResultsFor cases and effective R_t, all three time horizons outperform the three credible levels of the forecast. For hospitalizations, all three time horizons outperform the 20% and 50% credible intervals of the forecast. On the contrary, the 1-day and 3-day periods underperform the 90% credible intervals. Questions about uncertainty quantification should be re-calculated using the frequentist coverage probability of the Bayesian credible interval based on observed data for all three metrics.ConclusionsWe present an approach to automate the real-time estimation and forecasting of cases and hospitalizations and corresponding uncertainty using publicly available data. The models were able to infer short-term trends consistent with reported values at the HERC region level. Additionally, the models were able to accurately forecast and estimate the uncertainty of the measurements. This study can help identify the most affected regions and major outbreaks in the near future. The workflow can be adapted to other geographic regions, states, and even countries where decision-making processes are supported in real-time by the proposed modeling system.

Virulence differences of mpox (monkeypox) virus clades I, IIa, and IIb.1 in a small animal model

Human mpox (monkeypox), a disease with similarities to smallpox, is endemic in Africa where it has persisted as a zoonosis with limited human-to-human spread. Unexpectedly, the disease expanded globally in 2022 driven by human-to-human transmission outside of Africa. It is not yet known whether the latter is due solely to behavioral and environmental factors or whether the mpox virus is adapting to a new host. Genome sequencing has revealed differences between the current outbreak strains, classified as clade IIb, and the prior clade IIa and clade I viruses, but whether these differences contribute to virulence or transmission has not been determined. We demonstrate that the wild-derived inbred castaneous mouse provides an exceptional animal model for investigating clade differences in mpox virus virulence and show that the order is clade I > clade IIa > clade IIb.1. The greatly reduced replication of the clade IIb.1 major outbreak strain in mice and absence of lethality at 100 times the lethal dose of a closely related clade IIa virus, despite similar multiplication in cell culture, suggest that clade IIb is evolving diminished virulence or adapting to other species.

Persistence of a Frameshifting Deletion in SARS-CoV-2 ORF7a for the Duration of a Major Outbreak.

Australia experienced widespread COVID-19 outbreaks from infection with the SARS-CoV-2 Delta variant between June 2021 and February 2022. A 17-nucleotide frameshift-inducing deletion in ORF7a rapidly became represented at the consensus level (Delta-ORF7aΔ17del) in most Australian outbreak cases. Studies from early in the COVID-19 pandemic suggest that frameshift-inducing deletions in ORF7a do not persist for long in the population; therefore, Delta-ORF7aΔ17del genomes should have disappeared early in the Australian outbreak. In this study, we conducted a retrospective analysis of global Delta genomes to characterise the dynamics of Delta-ORF7aΔ17del over time, determined the frequency of all ORF7a deletions worldwide, and compared global trends with those of the Australian Delta outbreak. We downloaded all GISAID clade GK Delta genomes and scanned them for deletions in ORF7a. For each deletion we identified, we characterised its frequency, the number of countries it was found in, and how long it persisted. Of the 4,018,216 Delta genomes identified globally, 134,751 (~3.35%) possessed an ORF7a deletion, and ORF7aΔ17del was the most common. ORF7aΔ17del was the sole deletion in 28,014 genomes, of which 27,912 (~99.6%) originated from the Australian outbreak. During the outbreak, ~87% of genomes were Delta-ORF7aΔ17del, and genomes with this deletion were sampled until the outbreak's end. These data demonstrate that, contrary to suggestions early in the COVID-19 pandemic, genomes with frameshifting deletions in ORF7a can persist over long time periods. We suggest that the proliferation of Delta-ORF7aΔ17del genomes was likely a chance founder effect. Nonetheless, the frequency of ORF7a deletions in SARS-CoV-2 genomes worldwide suggests they might have some benefit for virus transmission.

Epidemiological Analysis of Surveillance Data on Reportable Priority Cattle Disease Outbreaks and Vaccination Coverage in West Arsi Zone, Oromia, Ethiopia

A retrospective study was conducted from July 2022 to September 2022 at West Arsi Zone, Oromia Region, Ethiopia, to identify major reportable cattle disease outbreaks and to understand the incidence, seasonal distribution, vaccination coverage and the trend of cattle disease outbreaks using passive surveillance data (DOVAR). From 1,560 DOVAR reports expected to be obtained at West Arsi Zone Agriculture Office Epidemiology Unit between 2012 and 2021, only 1,476 (11%) reports were available. A total of 1,496 cattle disease outbreaks with 1,889 cases and 346 deaths were recorded. The outbreaks were caused by Lumpy Skin Disease (LSD) (35.7%), Blackleg (21.43%), Bovine pasteurellosis (15.71%), Foot and Mouth disease (FMD) (14.3%), Anthrax (11.42%) and Rabies (1.43%) in decreasing order. LSD was responsible for the highest cases (30.2%) followed by FMD (23.9%) while blackleg and LSD were the leading causes of cattle deaths, which accounted for 34.4% and 28.9% of the deaths recorded, respectively. In contrast, the lowest number of outbreaks, cases and deaths of cattle was caused by rabies. Disease outbreaks occurred in all the seasons of the year; however, a relatively higher number of outbreaks (60%) were recorded in the early dry season (September to November). This study has shown a very low level of disease outbreak reporting rate in the West Arsi Zone, which may be due to no reporting or no outbreak of diseases. Generally, this study generates information that will be used as a baseline for further epidemiological studies for the development of sustainable programs for the control of cattle disease outbreaks in the study area.

Molecular detection and identification of fungal pathogens infections occurring in COVID-19 recovered patients.

The major outbreak of Corona virus disease COVID-19 caused by SARS-CoV-2 had brought about 4.55million deaths and had shaken the health care system all over the world. From the year 2020 the recovered COVID-19patients had started to develop microbial infection, most predominantly fungal infection in which Mucormycosis gained immediate attention as it has worsen the mortality rate in humans. In the present study of 53COVID-19recovered patients presented with microbial infection, the analysis of frequency distribution of fungal infection preponderantly with Rhizopus oryzae, followed by Aspergillus and Candida species.

Vector competence of three species of mosquitoes to Ingwavuma virus (Manzanilla orthobunyavirus), a new bunyavirus found circulating in India.

Ingwavuma virus (INGV), a mosquito-borne arbovirus reported from Africa and Southeast Asia has been found circulating in India as evidenced by virus isolation and antibody prevalence. INGV is now classified as Manzanilla orthobunyavirus belonging to family Peribunyaviridae. The virus is maintained in nature in a pig-mosquito-bird cycle. Human infection has been confirmed by virus isolation and detection of neutralizing antibodies. A study was initiated to determine the vector competence of Aedes aegypti, Culex quinquefasciatus, and Cx tritaeniorhynchus mosquitoes to INGV due to their high prevalence in India. Mosquitoes were oral fed on viraemic mice and INGV dissemination to legs, wings, salivary glands (saliva) was studied alongwith virus growth kinetics. The three mosquitoes replicated INGV with maximum titers of 3.7, 3.7 and 4.7log10TCID50/ml respectively and maintained the virus till 16th day post infection. However, vector competence and horizontal transmission to infant mouse was demonstrated only by Cx quinquefasciatus mosquitoes. Vertical or trans-ovarial transmission of INGV could not be demonstrated in the mosquito during the study. Though no major outbreak involving humans has been reported yet, the potential of the virus to replicate in different species of mosquitoes and vertebrates including humans pose a threat to public health should there be a change in its genome.

Augmenting Vaccine Efficacy against Delta Variant with 'Mycobacterium-w'-Mediated Modulation of NK-ADCC and TLR-MYD88 Pathways.

Mycobacterium-w (Mw) was shown to boost adaptive natural killer (ANK) cells and protect against COVID-19 during the first wave of the pandemic. As a follow-up of the trial, 50 healthcare workers (HCW) who had received Mw in September 2020 and subsequently received at least one dose of ChAdOx1 nCoV-19 vaccine (Mw + ChAdOx1 group) were monitored for symptomatic COVID-19 during a major outbreak with the delta variant of SARS-CoV-2 (April-June 2021), along with 201 HCW receiving both doses of the vaccine without Mw (ChAdOx1 group). Despite 48% having received just a single dose of the vaccine in the Mw + ChAdOx1 group, only two had mild COVID-19, compared to 36 infections in the ChAdOx1 group (HR-0.46, p = 0.009). Transcriptomic studies revealed an enhanced adaptive NK cell-dependent ADCC in the Mw + ChAdOx1 group, along with downregulation of the TLR2-MYD88 pathway and concomitant attenuation of downstream inflammatory pathways. This might have resulted in robust protection during the pandemic with the delta variant.

Outbreaks and sporadic cases of japanese encephalitis in the state of Odisha, India: Outcome of 7 years of laboratory surveillance.

Acute encephalitis syndrome (AES) is a major public health challenge in India. We report here the epidemiology of sporadics and outbreaks of Japanese Encephalitis (JE) in Odisha state during 2012-2018. A total of 4235 AES cases (sporadics - 3394, outbreak cases - 841) recorded including 42 outbreaks; majority (n = 18) of which were during 2016. Overall JE virus (JEV) positivity was 11.78% (outbreak cases - 24.5%, sporadic cases - 8.6%). Age ≤15 years were largely affected during outbreaks, while 16-60 years population was dominant among sporadics. The major outbreak (2016) involved 336 patients from a tribal dominated district, spread over 173 villages. JEV seropositivity was high (43.45%) with 28.57% mortality. Epidemiological linkage with pig rearing was documented through JEV neutralizing antibodies in 50% of pig serum samples. Although the postvaccination period (2017-18) showed increase in AES case reporting but low JE proportion. Ongoing surveillance and preparedness of the health system would be of importance, especially in tribal-dominated districts.

Development of real-time RT-PCR assays to detect measles virus on the Hologic Panther Fusion® System.

In 2019, Aotearoa New Zealand (NZ) experienced its worst measles outbreak since 1997. Due to declining childhood vaccination rates since the beginning of the SARS-CoV-2 pandemic, NZ is at serious risk of another major measles outbreak. Our laboratory provides diagnostic services to NZ's Southern region. In 2019 the Southern region experienced the greatest number of cases outside of Auckland and Northland, however we did not have a validated measles PCR assay in our laboratory. We sought to develop reverse transcription real-time polymerase chain reaction (RT-PCR) assays for measles on the Hologic Panther Fusion® System by utilising its open access function. Previously published real-time RT-PCR assays were modified and optimised to detect wild-type measles virus (LDT-Mea), and the vaccine strain of measles virus (LDT-MeaVacA), on the Hologic Panther Fusion® System. The assays were clinically validated. The LDT-Mea assay has a limit of detection (LoD) of 0.1 CCID50, while the LDT-MeaVacA assay is less sensitive with a LoD of 1 CCID50. Using 27 samples, the clinical sensitivity and specificity was 100% for both assays. Other common respiratory viruses were found not to cross-react with either the LDT-Mea or LDT-MeaVacA assays. We have successfully adapted and validated for diagnostic use on the Hologic Panther Fusion® System previously published assays to detect wild-type and vaccine strains of the measles virus. The implementation of measles testing on this system will greatly improve the turn-around time for measles testing, and better support the measles public health response, for our region.

Genomically Informed Custom Selective Enrichment of Shiga Toxigenic Escherichia coli (STEC) Outbreak Strains in Foods Using Antibiotics

Shiga toxigenic Escherichia coli (STEC) have been implicated in major foodborne outbreaks worldwide. The STEC family of pathogens is biochemically diverse, and current microbiological methods for detecting STEC are limited by the lack of a universal selective enrichment approach and prone to interference by high levels of background microbiota associated with certain types of foods. A novel approach has been developed for the recovery of foodborne illness outbreak strains during outbreak investigations based on the analysis of whole genome sequence data of implicated clinical isolates to determine antimicrobial resistance (AMR) genes. The presence of certain AMR genes in STEC has been correlated with the ability to grow in the presence of a specific antibiotic, which can be used to supplement enrichment broths to improve the recovery of a target strain. The enhanced recovery of STEC strains with different AMR profiles from various food types (beef, sprouts, leafy greens, and raw milk cheese) containing high levels of background microbiota was demonstrated using AMR predictions for nine different antibiotics. This genomically informed custom selective enrichment approach increases the availability of analytical options and improves the reliability of food microbiological analyses in confirming food vehicles implicated in outbreak events and defining the scope of product contamination to support risk assessment and risk management actions.

Standardising policy and technology responses in the immediate aftermath of a pandemic: a comparative and conceptual framework

BackgroundThe initial policy response to the COVID-19 pandemic has differed widely across countries. Such variability in government interventions has made it difficult for policymakers and health research systems to compare what has happened and the effectiveness of interventions across nations. Timely information and analysis are crucial to addressing the lag between the pandemic and government responses to implement targeted interventions to alleviate the impact of the pandemic.MethodsTo examine the effect government interventions and technological responses have on epidemiological and economic outcomes, this policy paper proposes a conceptual framework that provides a qualitative taxonomy of government policy directives implemented in the immediate aftermath of a pandemic announcement and before vaccines are implementable. This framework assigns a gradient indicating the intensity and extent of the policy measures and applies the gradient to four countries that share similar institutional features but different COVID-19 experiences: Italy, New Zealand, the United Kingdom and the United States of America.ResultsUsing the categorisation framework allows qualitative information to be presented, and more specifically the gradient can show the dynamic impact of policy interventions on specific outcomes. We have observed that the policy categorisation described here can be used by decision-makers to examine the impacts of major viral outbreaks such as SARS-CoV-2 on health and economic outcomes over time. The framework allows for a visualisation of the frequency and comparison of dominant policies and provides a conceptual tool to assess how dominant interventions (and innovations) affect different sets of health and non-health related outcomes during the response phase to the pandemic.ConclusionsPolicymakers and health researchers should converge toward an optimal set of policy interventions to minimize the costs of the pandemic (i.e., health and economic), and facilitate coordination across governance levels before effective vaccines are produced. The proposed framework provides a useful tool to direct health research system resources and build a policy benchmark for future viral outbreaks where vaccines are not readily available.

ZIKV: Epidemiology, infection mechanism and current therapeutics

The Zika virus (ZIKV) is a vector-borne flavivirus that has been detected in 87 countries worldwide. Outbreaks of ZIKV infection have been reported from various places around the world and the disease has been declared a public health emergency of international concern. ZIKV has two modes of transmission: vector and non-vector. The ability of ZIKV to vertically transmit in its competent vectors, such as Aedes aegypti and Aedes albopictus, helps it to cope with adverse conditions, and this could be the reason for the major outbreaks that occur from time to time. ZIKV outbreaks are a global threat and, therefore, there is a need for safe and effective drugs and vaccines to fight the virus. In more than 80% of cases, ZIKV infection is asymptomatic and leads to complications, such as microcephaly in newborns and Guillain–Barré syndrome (GBS) in adults. Drugs such as sofosbuvir, chloroquine, and suramin have been found to be effective against ZIKV infections, but further evaluation of their safety in pregnant women is needed. Although temoporfin can be given to pregnant women, it needs to be tested further for side effects. Many vaccine types based on protein, vector, DNA, and mRNA have been formulated. Some vaccines, such as mRNA-1325 and VRC-ZKADNA090-00-VP, have reached Phase II clinical trials. Some new techniques should be used for formulating and testing the efficacy of vaccines. Although there have been no recent outbreaks of ZIKV infection, several studies have shown continuous circulation of ZIKV in mosquito vectors, and there is a risk of re-emergence of ZIKV in the near future. Therefore, vaccines and drugs for ZIKV should be tested further, and safe and effective therapeutic techniques should be licensed for use during outbreaks.