A hospital-related outbreak of SARS-CoV-2 associated with variant Epsilon (B.1.429) in Taiwan: transmission potential and outbreak containment under intensified contact tracing, January-February 2021

A spatially-heterogeneous impact of fencing on the African swine fever wavefront in the Korean wild boar population

In October 2019, South Korea's first case of African swine fever (ASF) was reported in wild boar in the north of the country. Despite the implementation of a 2300 km-long fencing strategy, the ASF wavefront continued to invade southward. Our study aimed to investigate the ASF wavefront dynamics in different regions of South Korea, as well as to assess the effectiveness of the fencing measures on ASF dispersal and wavefront velocity. From the nationwide wild boar surveillance system, we extracted 2661 cases, starting from 2 October 2019 (first detection) to 15 September 2022. The cases were categorised into four main spatiotemporal clusters. The average wavefront velocity over the four clusters was estimated at 0.52 km/week, with the cluster in the eastern part of the Korean peninsula exhibiting the fastest velocity (0.99 km/week) compared to the other clusters (0.44, 0.31, and 0.15 km/week). We hypothesise that these differences are related to different wild boar densities due to heterogeneous habitat suitability. We also found that fencing significantly impacted ASF dispersal in only two of the four main clusters, with no evidence that fencing slowed down the spread of the wavefront in any of the clusters. We argue that this heterogeneity might result from fencing locations being misaligned with the true (and unobserved) wavefront.

Using real-time modelling to inform the 2017 Ebola outbreak response in DR Congo

Important policy questions during infections disease outbreaks include: i) How effective are particular interventions?; ii) When can resource-intensive interventions be removed? We used mathematical modelling to address these questions during the 2017 Ebola outbreak in Likati Health Zone, Democratic Republic of the Congo (DRC). Eight cases occurred before 15 May 2017, when the Ebola Response Team (ERT; co-ordinated by the World Health Organisation and DRC Ministry of Health) was deployed to reduce transmission. We used a branching process model to estimate that, pre-ERT arrival, the reproduction number was R = 1.49 (95% credible interval ( 0.67, 2.81 ) ). The risk of further cases occurring without the ERT was estimated to be 0.97 (97%). However, no cases materialised, suggesting that the ERT's measures were effective. We also estimated the risk of withdrawing the ERT in real-time. By the actual ERT withdrawal date (2 July 2017), the risk of future cases without the ERT was only 0.01, indicating that the ERT withdrawal decision was safe. We evaluated the sensitivity of our results to the estimated R value and considered different criteria for determining the ERT withdrawal date. This research provides an extensible modelling framework that can be used to guide decisions about when to relax interventions during future outbreaks.

Optimizing the timing of an end-of-outbreak declaration: Ebola virus disease in the Democratic Republic of the Congo

Following the apparent final case in an Ebola virus disease (EVD) outbreak, the decision to declare the outbreak over must balance societal benefits of relaxing interventions against the risk of resurgence. Estimates of the end-of-outbreak probability (the probability that no future cases will occur) provide quantitative evidence that can inform the timing of an end-of-outbreak declaration. An existing modeling approach for estimating the end-of-outbreak probability requires comprehensive contact tracing data describing who infected whom to be available, but such data are often unavailable or incomplete during outbreaks. Here, we develop a Markov chain Monte Carlo-based approach that extends the previous method and does not require contact tracing data. Considering data from two EVD outbreaks in the Democratic Republic of the Congo, we find that data describing who infected whom are not required to resolve uncertainty about when to declare an outbreak over.

Incubation Period and Serial Interval of Mpox in 2022 Global Outbreak Compared with Historical Estimates

Understanding changes in the transmission dynamics of mpox requires comparing recent estimates of key epidemiologic parameters with historical data. We derived historical estimates for the incubation period and serial interval for mpox and contrasted them with pooled estimates from the 2022 outbreak. Our findings show the pooled mean infection-to-onset incubation period was 8.1 days for the 2022 outbreak and 8.2 days historically, indicating the incubation periods remained relatively consistent over time, despite a shift in the major mode of transmission. However, we estimated the onset-to-onset serial interval at 8.7 days using 2022 data, compared with 14.2 days using historical data. Although the reason for this shortening of the serial interval is unclear, it may be because of increased public health interventions or a shift in the mode of transmission. Recognizing such temporal shifts is essential for informed response strategies, and public health measures remain crucial for controlling mpox and similar future outbreaks.

A simulation-based approach for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data

Tracking pathogen transmissibility during infectious disease outbreaks is essential for assessing the effectiveness of public health measures and planning future control strategies. A key measure of transmissibility is the time-dependent reproduction number, which has been estimated in real-time during outbreaks of a range of pathogens from disease incidence time series data. While commonly used approaches for estimating the time-dependent reproduction number can be reliable when disease incidence is recorded frequently, such incidence data are often aggregated temporally (for example, numbers of cases may be reported weekly rather than daily). As we show, commonly used methods for estimating transmissibility can be unreliable when the timescale of transmission is shorter than the timescale of data recording. To address this, here we develop a simulation-based approach involving Approximate Bayesian Computation for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data. We first use a simulated dataset representative of a situation in which daily disease incidence data are unavailable and only weekly summary values are reported, demonstrating that our method provides accurate estimates of the time-dependent reproduction number under such circumstances. We then apply our method to two outbreak datasets consisting of weekly influenza case numbers in 2019-20 and 2022-23 in Wales (in the United Kingdom). Our simple-to-use approach will allow accurate estimates of time-dependent reproduction numbers to be obtained from temporally aggregated data during future infectious disease outbreaks.

Preventing and controlling intra-hospital spread of COVID-19 in Taiwan - Looking back and moving forward

COVID-19 has exposed major weaknesses in the healthcare settings. The surge in COVID-19 cases increases the demands of health care, endangers vulnerable patients, and threats occupational safety. In contrast to a hospital outbreak of SARS leading to a whole hospital quarantined, at least 54 hospital outbreaks following a COVID-19 surge in the community were controlled by strengthened infection prevention and control measures for preventing transmission from community to hospitals as well as within hospitals. Access control measures include establishing triage, epidemic clinics, and outdoor quarantine stations. Visitor access restriction is applied to inpatients to limit the number of visitors. Health monitoring and surveillance is applied to healthcare personnel, including self-reporting travel declaration, temperature, predefined symptoms, and test results. Isolation of the confirmed cases during the contagious period and quarantine of the close contacts during the incubation period are critical for containment. The target populations and frequency of SARS-CoV-2 PCR and rapid antigen testing depend on the level of transmission. Case investigation and contact tracing should be comprehensive to identify the close contacts to prevent further transmission. These facility-based infection prevention and control strategies help reduce hospital transmission of SARS-CoV-2 to a minimum in Taiwan.

Achieving COVID-19 zero without lockdown, January 2020 to March 2022: The Taiwan model explained

Despite never imposing a lockdown, Taiwan achieved COVID-19 zero, with reporting only 56 local coronavirus disease 2019 (COVID-19) cases after testing 126,987 individuals in 2020, and further contained a large outbreak rapidly and successfully in 2021. At the onset of the COVID-19 pandemic, our infectious disease modeling results indicated that testing and contact tracing alone would fail to contain the pandemic. However, by supplementing this approach with general public surgical mask-wearing, the reproduction number (R0) could be suppressed to less than 1. This would effectively contain the virus's spread within Taiwan, particularly when combined with strict border control measures to prevent the overwhelming influx of imported COVID-19 cases and ensure the capacity of the medical and public health systems remains resilient. These modeling results became the theoretical basis behind the highly successful Taiwan model against COVID-19 during 2020-2021, supporting by negative excess mortality, seroepidemiological surveys, and molecular epidemiological analyses. This is a public health triumph demonstrating that a democratic and humane approach to the COVID-19 pandemic is not only feasible but highly effective. It also highlights the crucial role of infectious disease modeling in assisting the formulation of a successful national pandemic response.

Seroprevalence of Anti-SARS-CoV-2 Remained Extremely Low in Taiwan Until the Vaccination Program Was Implemented

Background

The Taiwanese government made a concerted effort to contain a coronavirus disease 2019 (COVID-19) nosocomial outbreak of variant B.1.429, shortly before universal vaccination program implementation. This study aimed to investigate seroprevalence in the highest-risk regions.

Methods

Between January and February 2021, we retrieved 10 000 repository serum samples from blood donors to examine for antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid (N) and spike (S) antigens. A positive result was confirmed if anti-N and anti-S antibodies were positive. Overall, 2000 donors residing in the highest-risk district and donating blood in January 2021 were further examined for SARS-CoV-2 RNA. We estimated seroprevalence and compared the epidemic curve between confirmed COVID-19 cases and blood donors with positive antibodies or viral RNA.

Results

Twenty-one cases with COVID-19 were confirmed in the nosocomial cluster, with an incidence of 1.27/100 000 in the COVID-affected districts. Among 4888 close contacts of the nosocomial cases, 20 (0.4%) became confirmed cases during isolation. Anti-SARS-CoV-2 was detected in 2 of the 10000 blood donors, showing a seroprevalence of 2/10000 (95% CI, 0.55-7.29). None of the 2000 donors who underwent tests for SARS-CoV-2 RNA were positive. The SARS-CoV-2 infection epidemic curve was observed sporadically in blood donors compared with the nosocomial cluster.

Conclusions

In early 2021, an extremely low anti-SARS-CoV-2 seroprevalence among blood donors was observed. Epidemic control measures through precise close contact tracing, testing, and isolation effectively contained SARS-CoV-2 transmission before universal vaccination program implementation.

COVID-19 public health surveillance and response in Taiwan: From containment to mitigation

Taiwan learned from its 2003 SARS experience and established multiple surveillance systems to be able to detect and respond to COVID-19. With the find, test, trace, isolate, and support (FTTIS) strategy, Taiwan was successful in containing SARS-CoV-2 from spreading for two years. During the surge of the Omicron variant in the community, COVID-19 control strategy shifted from containment to mitigation in April 2022, to reduce morbidity and mortality. Lessons learned from COVID-19 response re-emphasizes the importance of having sensitive public health surveillance and linking surveillance with public health actions.

Exact calculation of end-of-outbreak probabilities using contact tracing data

A key challenge for public health policymakers is determining when an infectious disease outbreak has finished. Following a period without cases, an estimate of the probability that no further cases will occur in future (the end-of-outbreak probability) can be used to inform whether or not to declare an outbreak over. An existing quantitative approach (the Nishiura method), based on a branching process transmission model, allows the end-of-outbreak probability to be approximated from disease incidence time series, the offspring distribution and the serial interval distribution. Here, we show how the end-of-outbreak probability under the same transmission model can be calculated exactly if data describing who-infected-whom (the transmission tree) are also available (e.g. from contact tracing studies). In that scenario, our novel approach (the traced transmission method) is straightforward to use. We demonstrate this by applying the method to data from previous outbreaks of Ebola virus disease and Nipah virus infection. For both outbreaks, the traced transmission method would have determined that the outbreak was over earlier than the Nishiura method. This highlights that collection of contact tracing data and application of the traced transmission method may allow stringent control interventions to be relaxed quickly at the end of an outbreak, with only a limited risk of outbreak resurgence.

Nanomaterials to combat SARS-CoV-2: Strategies to prevent, diagnose and treat COVID-19

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the associated coronavirus disease 2019 (COVID-19), which severely affect the respiratory system and several organs and tissues, and may lead to death, have shown how science can respond when challenged by a global emergency, offering as a response a myriad of rapid technological developments. Development of vaccines at lightning speed is one of them. SARS-CoV-2 outbreaks have stressed healthcare systems, questioning patients care by using standard non-adapted therapies and diagnostic tools. In this scenario, nanotechnology has offered new tools, techniques and opportunities for prevention, for rapid, accurate and sensitive diagnosis and treatment of COVID-19. In this review, we focus on the nanotechnological applications and nano-based materials (i.e., personal protective equipment) to combat SARS-CoV-2 transmission, infection, organ damage and for the development of new tools for virosurveillance, diagnose and immune protection by mRNA and other nano-based vaccines. All the nano-based developed tools have allowed a historical, unprecedented, real time epidemiological surveillance and diagnosis of SARS-CoV-2 infection, at community and international levels. The nano-based technology has help to predict and detect how this Sarbecovirus is mutating and the severity of the associated COVID-19 disease, thereby assisting the administration and public health services to make decisions and measures for preparedness against the emerging variants of SARS-CoV-2 and severe or lethal COVID-19.

Heterogeneity in the onwards transmission risk between local and imported cases affects practical estimates of the time-dependent reproduction number

During infectious disease outbreaks, inference of summary statistics characterizing transmission is essential for planning interventions. An important metric is the time-dependent reproduction number (Rt), which represents the expected number of secondary cases generated by each infected individual over the course of their infectious period. The value of Rt varies during an outbreak due to factors such as varying population immunity and changes to interventions, including those that affect individuals' contact networks. While it is possible to estimate a single population-wide Rt, this may belie differences in transmission between subgroups within the population. Here, we explore the effects of this heterogeneity on Rt estimates. Specifically, we consider two groups of infected hosts: those infected outside the local population (imported cases), and those infected locally (local cases). We use a Bayesian approach to estimate Rt, made available for others to use via an online tool, that accounts for differences in the onwards transmission risk from individuals in these groups. Using COVID-19 data from different regions worldwide, we show that different assumptions about the relative transmission risk between imported and local cases affect Rt estimates significantly, with implications for interventions. This highlights the need to collect data during outbreaks describing heterogeneities in transmission between different infected hosts, and to account for these heterogeneities in methods used to estimate Rt. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Transmission Dynamics and Effectiveness of Control Measures during COVID-19 Surge, Taiwan, April-August 2021

An unprecedented surge of COVID-19 cases in Taiwan in May 2021 led the government to implement strict nationwide control measures beginning May 15. During the surge, the government was able to bring the epidemic under control without a complete lockdown despite the cumulative case count reaching >14,400 and >780 deaths. We investigated the effectiveness of the public health and social measures instituted by the Taiwan government by quantifying the change in the effective reproduction number, which is a summary measure of the ability of the pathogen to spread through the population. The control measures that were instituted reduced the effective reproduction number from 2.0-3.3 to 0.6-0.7. This decrease was correlated with changes in mobility patterns in Taiwan, demonstrating that public compliance, active case finding, and contact tracing were effective measures in preventing further spread of the disease.

Systematic review and meta-analyses of superspreading of SARS-CoV-2 infections

Superspreading, or overdispersion in transmission, is a feature of SARS-CoV-2 transmission which results in surging epidemics and large clusters of infection. The dispersion parameter is a statistical parameter used to characterize and quantify heterogeneity. In the context of measuring transmissibility, it is analogous to measures of superspreading potential among populations by assuming that collective offspring distribution follows a negative-binomial distribution. We conducted a systematic review and meta-analysis on globally reported dispersion parameters of SARS-CoV-2 infection. All searches were carried out on 10 September 2021 in PubMed for articles published from 1 January 2020 to 10 September 2021. Multiple estimates of the dispersion parameter have been published for 17 studies, which could be related to where and when the data were obtained, in 8 countries (e.g. China, the United States, India, Indonesia, Israel, Japan, New Zealand and Singapore). High heterogeneity was reported among the included studies. The mean estimates of dispersion parameters range from 0.06 to 2.97 over eight countries, the pooled estimate was 0.55 (95% CI: 0.30, 0.79), with changing means over countries and decreasing slightly with the increasing reproduction number. The expected proportion of cases accounting for 80% of all transmissions is 19% (95% CrI: 7, 34) globally. The study location and method were found to be important drivers for diversity in estimates of dispersion parameters. While under high potential of superspreading, larger outbreaks could still occur with the import of the COVID-19 virus by traveling even when an epidemic seems to be under control.

A quantitative method to project the probability of the end of an epidemic: Application to the COVID-19 outbreak in Wuhan, 2020

The end-of-outbreak declaration is an important part of epidemic control, marking the relaxation or cancellation of prevention and control measures. We propose a probability model to retrospectively quantify the confidence of giving the end-of-outbreak declaration during the COVID-19 epidemic in early 2020 in Wuhan. By using the linear spline, we firstly estimates the time-varying proportion of cases who miss the nonpharmaceutical interventions (NPIs) among all reported cases. Assuming the reproduction numbers being 1.5, 2.0, 3.0, 4.0, 5.0 and 6.0, the respective probability of the end of the COVID-19 outbreak with time after the last reported case can be iteratively computed. Consequently, the varying reproduction numbers produce slightly different increasing patterns of NPI effectiveness, and the end-of-outbreak declarations with 95% confidence are projected consistently earlier than the day when the lockdown was actually lifted. The reason for the timing discrepancy is discussed as well.

Mitigating SARS-CoV-2 Transmission in Hospitals: A Systematic Literature Review

Objectives: Hospital outbreaks of SARS-CoV-2 infection are dreaded but preventable catastrophes. We review the literature to examine the pattern of SARS-CoV-2 transmission in hospitals and identify potential vulnerabilities to mitigate the risk of infection.

Methods: Three electronic databases (PubMed, Embase and Scopus) were searched from inception to July 27, 2021 for publications reporting SARS-CoV-2 outbreaks in hospital. Relevant articles and grey literature reports were hand-searched.

Results: Twenty-seven articles that described 35 SARS-CoV-2 outbreaks were included. Despite epidemiological investigations, the primary case could not be identified in 37% of outbreaks. Healthcare workers accounted for 40% of primary cases (doctors 17%, followed by ancillary staff 11%). Mortality among infected patients was approximately 15%. By contrast, none of the infected HCWs died. Several concerning patterns were identified, including infections involving ancillary staff and healthcare worker infections from the community and household contacts.

Conclusion: Continuous efforts to train-retrain and enforce correct personal protective equipment use and regular routine screening tests (especially among ancillary staff) are necessary to stem future hospital outbreaks of SARS-CoV-2.

Apigenin analogues as SARS-CoV-2 main protease inhibitors: In-silico screening approach

The COVID-19 new variants spread rapidly all over the world, and until now scientists strive to find virus-specific antivirals for its treatment. The main protease of SARS-CoV-2 (M^pro) exhibits high structural and sequence homology to main protease of SARS-CoV (93.23% sequence identity), and their sequence alignment indicated 12 mutated/variant residues. The sequence alignment of SARS-CoV-2 main protease led to identification of only one mutated/variant residue with no significant role in its enzymatic process. Therefore, M^pro was considered as a high-profile drug target in anti-SARS-CoV-2 drug discovery. Apigenin analogues to COVID-19 main protease binding were evaluated. The detailed interactions between the analogues of Apigenin and SARS-CoV-2 M^pro inhibitors were determined as hydrogen bonds, electronic bonds and hydrophobic interactions. The binding energies obtained from the molecular docking of M^pro with Boceprevir, Apigenin, Apigenin 7-glucoside-4’-p-coumarate, Apigenin 7-glucoside-4’-trans-caffeate and Apigenin 7-O-beta-d-glucoside (Cosmosiin) were found to be −6.6, −7.2, −8.8, −8.7 and −8.0 kcal/mol, respectively. Pharmacokinetic parameters and toxicological characteristics obtained by computational techniques and Virtual ADME studies of the Apigenin analogues confirmed that the Apigenin 7-glucoside-4’-p-coumarate is the best candidate for SARS-CoV-2 M^pro inhibition.

The risk of SARS-CoV-2 outbreaks in low prevalence settings following the removal of travel restrictions

Background

Countries around the world have introduced travel restrictions to reduce SARS-CoV-2 transmission. As vaccines are gradually rolled out, attention has turned to when travel restrictions and other non-pharmaceutical interventions (NPIs) can be relaxed.

Methods

Using SARS-CoV-2 as a case study, we develop a mathematical branching process model to assess the risk that, following the removal of NPIs, cases arriving in low prevalence settings initiate a local outbreak. Our model accounts for changes in background population immunity due to vaccination. We consider two locations with low prevalence in which the vaccine rollout has progressed quickly – specifically, the Isle of Man (a British crown dependency in the Irish Sea) and the country of Israel.

Results

We show that the outbreak risk is unlikely to be eliminated completely when travel restrictions and other NPIs are removed. This general result is the most important finding of this study, rather than exact quantitative outbreak risk estimates in different locations. It holds even once vaccine programmes are completed. Key factors underlying this result are the potential for transmission even following vaccination, incomplete vaccine uptake, and the recent emergence of SARS-CoV-2 variants with increased transmissibility.

Conclusions

Combined, the factors described above suggest that, when travel restrictions are relaxed, it may still be necessary to implement surveillance of incoming passengers to identify infected individuals quickly. This measure, as well as tracing and testing (and/or isolating) contacts of detected infected passengers, remains useful to suppress potential outbreaks while global case numbers are high.

The effectiveness of public health measures against COVID-19 has varied between countries, with some experiencing many infections and others containing transmission successfully. As vaccines are deployed, an important challenge is deciding when to relax measures. Here, we consider locations with few cases, and investigate whether vaccination can ever eliminate the risk of COVID-19 outbreaks completely, allowing measures to be removed risk-free. Using a mathematical model, we demonstrate that there is still a risk that imported cases initiate outbreaks when measures are removed, even if most of the population is fully vaccinated. This highlights the need for continued vigilance in low prevalence settings to prevent imported cases leading to local transmission. Until case numbers are reduced globally, so that SARS-CoV-2 spread between countries is less likely, the risk of outbreaks in low prevalence settings will remain.

Sachak-Patwa et al. estimate the risk of SARS-CoV-2 outbreaks in low prevalence settings following the removal of travel restrictions and other non-pharmaceutical interventions, with the Isle of Man and Israel as case studies. Using a branching process mathematical model, the authors show that even after a large proportion of the population is vaccinated, there remains a risk of local outbreaks from imported cases.

Impending disaster: rise of the epsilon variant in Pakistan and the way forward

The constantly mutating severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) does not appear to be slowing down any time soon. All countries, particularly developing countries, must adapt and strategically plan their way of life around the pandemic, while doing everything possible to keep the mortality rate and spread of the newly emerging variants as low as possible, in order to avoid a further blow to the economy and way of life. Pakistan is one such developing country that is currently battling the dangerous delta strain of SARS-COV-2 with limited resources and has recently seen the emergence of an equally transmissible and highly infectious epsilon strain. This is a concerning situation considering that Pakistan's already overburdened health system and faltering economy cannot withstand another dangerous SARS-COV-2 variant attack. This article highlights some strategies for the country to fortify its defences to prevent the epsilon variant from spreading before it is too late, and emphasises that while identifying potential immune evasion mechanisms in SARS-COV-2 variants is critical in the fight against COVID-19, it is also critical to develop methods of efficient and cost-effective detection to identify an early outbreak and then vigilantly and systematically plan area lockdowns before any hope of conquering this pandemic is lost.