Characterizing the transmission potential of zoonotic infections from minor outbreaks

"Mpox in MSM: Tackling Stigma, Minimizing Risk Factors, Exploring Pathogenesis, and Treatment Approaches"

Mpox is a zoonotic disease caused by the monkeypox virus (MPV), primarily found in Central and West African countries. The typical presentation of the disease before the 2022 mpox outbreak includes a febrile prodrome 5-13 days post-exposure, accompanied by lymphadenopathy, malaise, headache, and muscle aches. Unexpectedly, during the 2022 outbreak, several cases of atypical presentations of the disease were reported, such as the absence of prodromal symptoms and the presence of genital skin lesions suggestive of sexual transmission. As per the World Health Organization (WHO), as of March 20, 2024, 94,707 cases of mpox were reported worldwide, resulting in 181 deaths (22 in African endemic regions and 159 in non-endemic countries). The United States Centers for Disease Control and Prevention (CDC) reports a total of 32,063 cases (33.85% of total cases globally), with 58 deaths (32.04% of global deaths) due to mpox. Person-to-person transmission of mpox can occur through respiratory droplets and sustained close contact. However, during the 2022 outbreak of mpox, a high incidence of anal and perianal lesions among MSMs indicated sexual transmission of MPV as a major route of transmission. Since MSMs are disproportionately at risk for HIV transmission, this review discusses the risk factors, transmission patterns, pathogenesis, vaccine, and treatment options for mpox among MSM and people living with HIV (PLWH). Furthermore, we provide a brief perspective on the evolution of the MPV in immunocompromised people like PLWH.

A beta-Poisson model for infectious disease transmission

Outbreaks of emerging and zoonotic infections represent a substantial threat to human health and well-being. These outbreaks tend to be characterised by highly stochastic transmission dynamics with intense variation in transmission potential between cases. The negative binomial distribution is commonly used as a model for transmission in the early stages of an epidemic as it has a natural interpretation as the convolution of a Poisson contact process and a gamma-distributed infectivity. In this study we expand upon the negative binomial model by introducing a beta-Poisson mixture model in which infectious individuals make contacts at the points of a Poisson process and then transmit infection along these contacts with a beta-distributed probability. We show that the negative binomial distribution is a limit case of this model, as is the zero-inflated Poisson distribution obtained by combining a Poisson-distributed contact process with an additional failure probability. We assess the beta-Poisson model's applicability by fitting it to secondary case distributions (the distribution of the number of subsequent cases generated by a single case) estimated from outbreaks covering a range of pathogens and geographical settings. We find that while the beta-Poisson mixture can achieve a closer to fit to data than the negative binomial distribution, it is consistently outperformed by the negative binomial in terms of Akaike Information Criterion, making it a suboptimal choice on parsimonious grounds. The beta-Poisson performs similarly to the negative binomial model in its ability to capture features of the secondary case distribution such as overdispersion, prevalence of superspreaders, and the probability of a case generating zero subsequent cases. Despite this possible shortcoming, the beta-Poisson distribution may still be of interest in the context of intervention modelling since its structure allows for the simulation of measures which change contact structures while leaving individual-level infectivity unchanged, and vice-versa.

Monkeypox in Latin America and the Caribbean: assessment of the first 100 days of the 2022 outbreak

During the 2022 monkeypox (mpox) epidemic's first 100 days, 99 non-endemic countries, including 25 Latin American and Caribbean (LAC) countries, reported >64,000 cases. We aim to assess the cases' introduction, epidemiological profile, initial response, transmission dynamics, and main challenges ahead among LAC countries during the first 100 days of the mpox 2022 epidemic. We used mixed methods, including desktop research and open data analysis. The 2022 mpox epidemic has progressed consistently through LAC, with Brazil and Peru combining for over 80% of the confirmed LAC cases. Although Brazil reports the highest mpox case counts (n = 4472), Peru reports the highest incidence (41 confirmed cases per 1 million inhabitants). Initially, LAC missed the opportunity to focus on the high-risk population, including the people living with HIV (PLHIV) and gay, bisexual, and men who have sex with men (GBMSM). Moreover, the main challenges ahead include stigmatization, vaccine inequity, barriers to accessing diagnostics, and complete isolation. Furthermore, we estimated that Colombia, Brazil, the United States, and Peru are the world frontrunners in mpox duplication time (estimated between 6.4 and 8.8) and effective reproductive number (estimated between 2.7 and 3.8). In addition, Brazil reported its first case of inverse zoonosis in a dog and Peru its first autochthonous MPXV lineage, B.1.6. LAC has become the epicenter of the 2022 mpox epidemic, with Brazil and Peru emerging as the new mpox hot zones. Therefore, LAC countries must join efforts to control this epidemic and overcome the challenges of vaccine inequity and stigmatization.

Mathematical modeling of mpox: A scoping review

Background

Mpox (monkeypox), a disease historically endemic to Africa, has seen its largest outbreak in 2022 by spreading to many regions of the world and has become a public health threat. Informed policies aimed at controlling and managing the spread of this disease necessitate the use of adequate mathematical modeling strategies.

Objective

In this scoping review, we sought to identify the mathematical models that have been used to study mpox transmission in the literature in order to determine what are the model classes most frequently used, their assumptions, and the modelling gaps that need to be addressed in the context of the epidemiological characteristics of the ongoing mpox outbreak.

Methods

This study employed the methodology of the PRISMA guidelines for scoping reviews to identify the mathematical models available to study mpox transmission dynamics. Three databases (PubMed, Web of Science and MathSciNet) were systematically searched to identify relevant studies.

Results

A total of 5827 papers were screened from the database queries. After the screening, 35 studies that met the inclusion criteria were analyzed, and 19 were finally included in the scoping review. Our results show that compartmental, branching process, Monte Carlo (stochastic), agent-based, and network models have been used to study mpox transmission dynamics between humans as well as between humans and animals. Furthermore, compartmental and branching models have been the most commonly used classes.

Conclusions

There is a need to develop modeling strategies for mpox transmission that take into account the conditions of the current outbreak, which has been largely driven by human-to-human transmission in urban settings. In the current scenario, the assumptions and parameters used by most of the studies included in this review (which are largely based on a limited number of studies carried out in Africa in the early 80s) may not be applicable, and therefore, can complicate any public health policies that are derived from their estimates. The current mpox outbreak is also an example of how more research into neglected zoonoses is needed in an era where new and re-emerging diseases have become global public health threats.

Quantifying the future risk of dengue under climate change in Japan

Background

In metropolitan Tokyo in 2014, Japan experienced its first domestic dengue outbreak since 1945. The objective of the present study was to quantitatively assess the future risk of dengue in Japan using climate change scenarios in a high-resolution geospatial environment by building on a solid theory as a baseline in consideration of future adaptation strategies.

Methods

Using climate change scenarios of the Model for Interdisciplinary Research on Climate version 6 (MIROC6), representative concentration pathway (RCP) 2.6, 4.5, and 8.5, we computed the daily average temperature and embedded this in the effective reproduction number of dengue, R(T), to calculate the extinction probability and interepidemic period across Japan.

Results

In June and October, the R(T) with daily average temperature T, was <1 as in 2022; however, an elevation in temperature increased the number of days with R(T) >1 during these months under RCP8.5. The time period with a risk of dengue transmission gradually extended to late spring (April–May) and autumn (October–November). Under the RCP8.5 scenario in 2100, the possibility of no dengue-free months was revealed in part of southernmost Okinawa Prefecture, and the epidemic risk extended to the entire part of northernmost Hokkaido Prefecture.

Conclusion

Each locality in Japan must formulate action plans in response to the presented scenarios. Our geographic analysis can help local governments to develop adaptation policies that include mosquito breeding site elimination, distribution of adulticides and larvicides, and elevated situation awareness to prevent transmission via bites from Aedes vectors.

Containment, Contact Tracing and Asymptomatic Transmission of Novel Coronavirus Disease (COVID-19): A Modelling Study

When a novel infectious disease emerges, enhanced contact tracing and isolation are implemented to prevent a major epidemic, and indeed, they have been successful for the control of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), which have been greatly reduced without causing a global pandemic. Considering that asymptomatic and pre-symptomatic infections are substantial for the novel coronavirus disease (COVID-19), the feasibility of preventing the major epidemic has been questioned. Using a two-type branching process model, the present study assesses the feasibility of containing COVID-19 by computing the probability of a major epidemic. We show that if there is a substantial number of asymptomatic transmissions, cutting chains of transmission by means of contact tracing and case isolation would be very challenging without additional interventions, and in particular, untraced cases contribute to lowering the feasibility of containment. Even if isolation of symptomatic cases is conducted swiftly after symptom onset, only secondary transmissions after the symptom onset can be prevented.

Controlling emerging zoonoses at the animal-human interface

BACKGROUND

For many emerging or re-emerging pathogens, cases in humans arise from a mixture of introductions (via zoonotic spillover from animal reservoirs or geographic spillover from endemic regions) and secondary human-to-human transmission. Interventions aiming to reduce incidence of these infections can be focused on preventing spillover or reducing human-to-human transmission, or sometimes both at once, and typically are governed by resource constraints that require policymakers to make choices. Despite increasing emphasis on using mathematical models to inform disease control policies, little attention has been paid to guiding rational disease control at the animal-human interface.

METHODS

We introduce a modeling framework to analyze the impacts of different disease control policies, focusing on pathogens exhibiting subcritical transmission among humans (i.e. pathogens that cannot establish sustained human-to-human transmission). We quantify the relative effectiveness of measures to reduce spillover (e.g. reducing contact with animal hosts), human-to-human transmission (e.g. case isolation), or both at once (e.g. vaccination), across a range of epidemiological contexts.

RESULTS

We provide guidelines for choosing which mode of control to prioritize in different epidemiological scenarios and considering different levels of resource and relative costs. We contextualize our analysis with current zoonotic pathogens and other subcritical pathogens, such as post-elimination measles, and control policies that have been applied.

CONCLUSIONS

Our work provides a model-based, theoretical foundation to understand and guide policy for subcritical zoonoses, integrating across disciplinary and species boundaries in a manner consistent with One Health principles.

Human monkeypox - After 40 years, an unintended consequence of smallpox eradication

Smallpox eradication, coordinated by the WHO and certified 40 years ago, led to the cessation of routine smallpox vaccination in most countries. It is estimated that over 70% of the world's population is no longer protected against smallpox, and through cross-immunity, to closely related orthopox viruses such as monkeypox. Monkeypox is now a re-emerging disease. Monkeypox is endemic in as yet unconfirmed animal reservoirs in sub-Saharan Africa, while its human epidemiology appears to be changing. Monkeypox in small animals imported from Ghana as exotic pets was at the origin of an outbreak of human monkeypox in the USA in 2003. Travellers infected in Nigeria were at the origin of monkeypox cases in the UK in 2018 and 2019, Israel in 2018 and Singapore in2019. Together with sporadic reports of human infections with other orthopox viruses, these facts invite speculation that emergent or re-emergent human monkeypox might fill the epidemiological niche vacated by smallpox. An ad-hoc and unofficial group of interested experts met to consider these issues at Chatham House, London in June 2019, in order to review available data and identify monkeypox-related research gaps. Gaps identified by the experts included:The experts further agreed on the need for a better understanding of the genomic evolution and changing epidemiology of orthopox viruses, the usefulness of in-field genomic diagnostics, and the best disease control strategies, including the possibility of vaccination with new generation non-replicating smallpox vaccines and treatment with recently developed antivirals.

Estimation of country-level basic reproductive ratios for novel Coronavirus (SARS-CoV-2/COVID-19) using synthetic contact matrices

The 2019-2020 pandemic of atypical pneumonia (COVID-19) caused by the virus SARS-CoV-2 has spread globally and has the potential to infect large numbers of people in every country. Estimating the country-specific basic reproductive ratio is a vital first step in public-health planning. The basic reproductive ratio (R0) is determined by both the nature of pathogen and the network of human contacts through which the disease can spread, which is itself dependent on population age structure and household composition. Here we introduce a transmission model combining age-stratified contact frequencies with age-dependent susceptibility, probability of clinical symptoms, and transmission from asymptomatic (or mild) cases, which we use to estimate the country-specific basic reproductive ratio of COVID-19 for 152 countries. Using early outbreak data from China and a synthetic contact matrix, we estimate an age-stratified transmission structure which can then be extrapolated to 151 other countries for which synthetic contact matrices also exist. This defines a set of country-specific transmission structures from which we can calculate the basic reproductive ratio for each country. Our predicted R0 is critically sensitive to the intensity of transmission from asymptomatic cases; with low asymptomatic transmission the highest values are predicted across Eastern Europe and Japan and the lowest across Africa, Central America and South-Western Asia. This pattern is largely driven by the ratio of children to older adults in each country and the observed propensity of clinical cases in the elderly. If asymptomatic cases have comparable transmission to detected cases, the pattern is reversed. Our results demonstrate the importance of age-specific heterogeneities going beyond contact structure to the spread of COVID-19. These heterogeneities give COVID-19 the capacity to spread particularly quickly in countries with older populations, and that intensive control measures are likely to be necessary to impede its progress in these countries.

Probabilistic reconstruction of measles transmission clusters from routinely collected surveillance data

Pockets of susceptibility resulting from spatial or social heterogeneity in vaccine coverage can drive measles outbreaks, as cases imported into such pockets are likely to cause further transmission and lead to large transmission clusters. Characterizing the dynamics of transmission is essential for identifying which individuals and regions might be most at risk. As data from detailed contact-tracing investigations are not available in many settings, we developed an R package called o2geosocial to reconstruct the transmission clusters and the importation status of the cases from their age, location, genotype and onset date. We compared our inferred cluster size distributions to 737 transmission clusters identified through detailed contact-tracing in the USA between 2001 and 2016. We were able to reconstruct the importation status of the cases and found good agreement between the inferred and reference clusters. The results were improved when the contact-tracing investigations were used to set the importation status before running the model. Spatial heterogeneity in vaccine coverage is difficult to measure directly. Our approach was able to highlight areas with potential for local transmission using a minimal number of variables and could be applied to assess the intensity of ongoing transmission in a region.

Estimation of country-level basic reproductive ratios for novel Coronavirus (SARS-CoV-2/COVID-19) using synthetic contact matrices

The 2019-2020 pandemic of atypical pneumonia (COVID-19) caused by the virus SARS-CoV-2 has spread globally and has the potential to infect large numbers of people in every country. Estimating the country-specific basic reproductive ratio is a vital first step in public-health planning. The basic reproductive ratio (R0) is determined by both the nature of pathogen and the network of human contacts through which the disease can spread, which is itself dependent on population age structure and household composition. Here we introduce a transmission model combining age-stratified contact frequencies with age-dependent susceptibility, probability of clinical symptoms, and transmission from asymptomatic (or mild) cases, which we use to estimate the country-specific basic reproductive ratio of COVID-19 for 152 countries. Using early outbreak data from China and a synthetic contact matrix, we estimate an age-stratified transmission structure which can then be extrapolated to 151 other countries for which synthetic contact matrices also exist. This defines a set of country-specific transmission structures from which we can calculate the basic reproductive ratio for each country. Our predicted R0 is critically sensitive to the intensity of transmission from asymptomatic cases; with low asymptomatic transmission the highest values are predicted across Eastern Europe and Japan and the lowest across Africa, Central America and South-Western Asia. This pattern is largely driven by the ratio of children to older adults in each country and the observed propensity of clinical cases in the elderly. If asymptomatic cases have comparable transmission to detected cases, the pattern is reversed. Our results demonstrate the importance of age-specific heterogeneities going beyond contact structure to the spread of COVID-19. These heterogeneities give COVID-19 the capacity to spread particularly quickly in countries with older populations, and that intensive control measures are likely to be necessary to impede its progress in these countries.

Comparison of methods to Estimate Basic Reproduction Number (R 0) of influenza, Using Canada 2009 and 2017-18 A (H1N1) Data

Background

The basic reproduction number (R ₀) has a key role in epidemics and can be utilized for preventing epidemics. In this study, different methods are used for estimating R ₀'s and their vaccination coverage to find the formula with the best performance.

Materials and Methods

We estimated R ₀ for cumulative cases count data from April 18 to July 6, 2009 and 35-2017 to 34-2018 weeks in Canada: maximum likelihood (ML), exponential growth rate (EG), time-dependent reproduction numbers (TD), attack rate (AR), gamma-distributed generation time (GT), and the final size of the epidemic. Gamma distribution with mean and standard deviation 3.6 ± 1.4 is used as GT.

Results

The AR method obtained a R _{0 (}95% confidence interval [CI]) value of 1.116 (1.1163, 1.1165) and an EG (95%CI) value of 1.46 (1.41, 1.52). The R ₀ (95%CI) estimate was 1.42 (1.27, 1.57) for the obtained ML, 1.71 (1.12, 2.03) for the obtained TD, 1.49 (1.0, 1.97) for the gamma-distributed GT, and 1.00 (0.91, 1.09) for the final size of the epidemic. The minimum and maximum vaccination coverage were related to AR and TD methods, respectively, where the TD method has minimum mean squared error (MSE). Finally, the R ₀ (95%CI) for 2018 data was 1.52 (1.11, 1.94) by TD method, and vaccination coverage was estimated as 34.2%.

Conclusion

For the purposes of our study, the estimation of TD was the most useful tool for computing the R ₀, because it has the minimum MSE. The estimation R ₀ > 1 indicating that the epidemic has occurred. Thus, it is required to vaccinate at least 41.5% to prevent and control the next epidemic.

Sub- or supercritical transmissibilities in a finite disease outbreak: Symmetry in outbreak properties of a disease conditioned on extinction

This work is concerned with the transmissibility of a disease, on observation of an outbreak of limited size. When such an outbreak occurs, an accurate estimate of the transmissibility of the responsible pathogen is essential for an appropriate response to future outbreaks. Transmissibility is usually characterised in terms of the reproduction number, R, which is the mean number of new cases of infection produced by a single infectious individual. A subcritical reproduction number (R < 1) guarantees that an outbreak will eventually die out of its own accord. By contrast, a supercritical reproduction number (R > 1) does not guarantee spread of the disease, since even with appreciable transmissibility, an outbreak may become extinct due to stochastic effects associated with a small number of infected individuals. Once the number of infectious individuals is conditioned on extinction, we show that an exact symmetry of the underlying theory ensures two distinct values of R, one larger than unity, the other smaller than unity, for which all outbreak properties are identical, with no signature of difference. Therefore a disease with a subcritical R, or its supercritical counterpart, when conditioned on extinction, have, for a given outbreak, identical individual likelihoods. In the full likelihood, this symmetry is lost, since the individual likelihood for a subcritical R is weighted by an extinction probability of unity, but the individual likelihood of a supercritical R is weighted by a sub-unity extinction probability. However, the inference can still benefit from the underlying symmetry, since it yields a mapping of all supercritical reproduction numbers onto the subcritical domain (R < 1), thereby speeding up evaluation of the likelihood profile. The symmetry holds in the standard situation, where the distribution of secondary cases is Poisson, as well as where this distribution has a negative binomial form and super-spreading can occur.

A primer on the use of probability generating functions in infectious disease modeling

We explore the application of probability generating functions (PGFs) to invasive processes, focusing on infectious disease introduced into large populations. Our goal is to acquaint the reader with applications of PGFs, moreso than to derive new results. PGFs help predict a number of properties about early outbreak behavior while the population is still effectively infinite, including the probability of an epidemic, the size distribution after some number of generations, and the cumulative size distribution of non-epidemic outbreaks. We show how PGFs can be used in both discrete-time and continuous-time settings, and discuss how to use these results to infer disease parameters from observed outbreaks. In the large population limit for susceptible-infected-recovered (SIR) epidemics PGFs lead to survival-function based models that are equivalent to the usual mass-action SIR models but with fewer ODEs. We use these to explore properties such as the final size of epidemics or even the dynamics once stochastic effects are negligible. We target this primer at biologists and public health researchers with mathematical modeling experience who want to learn how to apply PGFs to invasive diseases, but it could also be used in an applications-based mathematics course on PGFs. We include many exercises to help demonstrate concepts and to give practice applying the results. We summarize our main results in a few tables. Additionally we provide a small python package which performs many of the relevant calculations.

Going through the motions: incorporating movement analyses into disease research

Though epidemiology dates back to the 1700s, most mathematical representations of epidemics still use transmission rates averaged at the population scale, especially for wildlife diseases. In simplifying the contact process, we ignore the heterogeneities in host movements that complicate the real world, and overlook their impact on spatiotemporal patterns of disease burden. Movement ecology offers a set of tools that help unpack the transmission process, letting researchers more accurately model how animals within a population interact and spread pathogens. Analytical techniques from this growing field can also help expose the reverse process: how infection impacts movement behaviours, and therefore other ecological processes like feeding, reproduction, and dispersal. Here, we synthesise the contributions of movement ecology in disease research, with a particular focus on studies that have successfully used movement-based methods to quantify individual heterogeneity in exposure and transmission risk. Throughout, we highlight the rapid growth of both disease and movement ecology and comment on promising but unexplored avenues for research at their overlap. Ultimately, we suggest, including movement empowers ecologists to pose new questions, expanding our understanding of host-pathogen dynamics and improving our predictive capacity for wildlife and even human diseases.

Integrative modelling for One Health: pattern, process and participation

This paper argues for an integrative modelling approach for understanding zoonoses disease dynamics, combining process, pattern and participatory models. Each type of modelling provides important insights, but all are limited. Combining these in a '3P' approach offers the opportunity for a productive conversation between modelling efforts, contributing to a 'One Health' agenda. The aim is not to come up with a composite model, but seek synergies between perspectives, encouraging cross-disciplinary interactions. We illustrate our argument with cases from Africa, and in particular from our work on Ebola virus and Lassa fever virus. Combining process-based compartmental models with macroecological data offers a spatial perspective on potential disease impacts. However, without insights from the ground, the 'black box' of transmission dynamics, so crucial to model assumptions, may not be fully understood. We show how participatory modelling and ethnographic research of Ebola and Lassa fever can reveal social roles, unsafe practices, mobility and movement and temporal changes in livelihoods. Together with longer-term dynamics of change in societies and ecologies, all can be important in explaining disease transmission, and provide important complementary insights to other modelling efforts. An integrative modelling approach therefore can offer help to improve disease control efforts and public health responses.This article is part of the themed issue 'One Health for a changing world: zoonoses, ecosystems and human well-being'.

A pandemic risk assessment of middle east respiratory syndrome coronavirus (MERS-CoV) in Saudi Arabia

Since the initial emergence of Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012, a high incidence rate has been observed in Saudi Arabia. This suggests that the country is at continuous risk. The epidemic level of MERS-CoV infection was examined in Saudi Arabia by the Susceptible-Infectious-Recovered (SIR) model using a Bayesian approach for estimation of time dependent reproduction number (R) across a two-year interval (May, 2013-May, 2015) in five defined clusters, followed by sensitivity analysis of the most significant clusters. Significant MERS-CoV peaks were detected in the period between March and May of each year. Moreover, MERS-CoV infection was highlighted in western (40.8%) and central (31.9%) regions, followed by eastern region (20%). The temporal-based Bayesian approach indicated a sub-critical epidemic in all regions in the baseline scenario (R: 0.85-0.97). However, R potential limit was exceeded in the sensitivity analysis scenario in only central and western regions (R: 1.08-1.12) that denoted epidemic level in those regions. The impact of sporadic cases was found relatively insignificant and pinpointed to the lack of zoonotic influence on MERS-CoV transmission dynamics. The results of current study would be helpful for evaluation of future progression of MERS-CoV infections, better understanding and control interventions.

The global spread of Middle East respiratory syndrome: an analysis fusing traditional epidemiological tracing and molecular phylodynamics

BACKGROUND

Since its discovery in 2012, over 1700 confirmed cases of Middle East Respiratory Syndrome (MERS) have been documented worldwide and more than a third of those cases have died. While the greatest number of cases has occurred in Saudi Arabia, the recent export of MERS-coronavirus (MERS-CoV) to Republic of Korea showed that a pandemic is a possibility that cannot be ignored. Due to the deficit of knowledge in transmission methodology, targeted treatment and possible vaccines, understanding this virus should be a priority. Our aim was to combine epidemiological data from literature with genetic information from viruses sequenced around the world to present a phylodynamic picture of MERS spread molecular level to global scale.

METHODS

We performed a qualitative meta-analysis of all laboratory confirmed cases worldwide to date based on literature, with emphasis on international transmission and healthcare associated infections. In parallel, we used publicly available MERS-CoV genomes from GenBank to create a phylogeographic tree, detailing geospatial timeline of viral evolution.

RESULTS

Several healthcare associated outbreaks starting with the retrospectively identified hospital outbreak in Jordan to the most recent outbreak in Riyadh, Saudi Arabia have occurred. MERS has also crossed many oceans, entering multiple nations in eight waves between 2012 and 2015. In this paper, the spatiotemporal history of MERS cases, as documented epidemiologically, was examined by Bayesian phylogenetic analysis. Distribution of sequences into geographic clusters and interleaving of MERS-CoV sequences from camels among those isolated from humans indicated that multiple zoonotic introductions occurred in endemic nations. We also report a summary of basic reproduction numbers for MERS-CoV in humans and camels.

CONCLUSION

Together, these analyses can help us identify factors associated with viral evolution and spread as well as establish efficacy of infection control measures. The results are especially pertinent to countries without current MERS-CoV endemic, since their unfamiliarity makes them particularly susceptible to uncontrollable spread of a virus that may be imported by travelers.

2015 Middle East Respiratory Syndrome Coronavirus (MERS-CoV) nosocomial outbreak in South Korea: insights from modeling

Background. Since the emergence of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) in 2012, more than 1,300 laboratory confirmed cases of MERS-CoV infections have been reported in Asia, North Africa, and Europe by July 2015. The recent MERS-CoV nosocomial outbreak in South Korea quickly became the second largest such outbreak with 186 total cases and 36 deaths in a little more than one month, second only to Saudi Arabia in country-specific number of reported cases. Methods. We use a simple mathematical model, the Richards model, to trace the temporal course of the South Korea MERS-CoV outbreak. We pinpoint its outbreak turning point and its transmissibility via basic reproduction number R 0 in order to ascertain the occurrence of this nosocomial outbreak and how it was quickly brought under control. Results. The estimated outbreak turning point of ti = 23.3 days (95% CI [22.6-24.0]), or 23-24 days after the onset date of the index case on May 11, pinpoints June 3-4 as the time of the turning point or the peak incidence for this outbreak by onset date. R 0 is estimated to range between 7.0 and 19.3. Discussion and Conclusion. The turning point of the South Korea MERS-CoV outbreak occurred around May 27-29, when control measures were quickly implemented after laboratory confirmation of the first cluster of nosocomial infections by the index patient. Furthermore, transmissibility of MERS-CoV in the South Korea outbreak was significantly higher than those reported from past MERS-CoV outbreaks in the Middle East, which is attributable to the nosocomial nature of this outbreak. Our estimate of R 0 for the South Korea MERS-CoV nosocomial outbreak further highlights the importance and the risk involved in cluster infections and superspreading events in crowded settings such as hospitals. Similar to the 2003 SARS epidemic, outbreaks of infectious diseases with low community transmissibility like MERS-CoV could still occur initially with large clusters of nosocomial infections, but can be quickly and effectively controlled with timely intervention measures.

The pandemic potential of avian influenza A(H7N9) virus: a review

In March 2013 the first cases of human avian influenza A(H7N9) were reported to the World Health Organization. Since that time, over 650 cases have been reported. Infections are associated with considerable morbidity and mortality, particularly within certain demographic groups. This rapid increase in cases over a brief time period is alarming and has raised concerns about the pandemic potential of the H7N9 virus. Three major factors influence the pandemic potential of an influenza virus: (1) its ability to cause human disease, (2) the immunity of the population to the virus, and (3) the transmission potential of the virus. This paper reviews what is currently known about each of these factors with respect to avian influenza A(H7N9). Currently, sustained human-to-human transmission of H7N9 has not been reported; however, population immunity to the virus is considered very low, and the virus has significant ability to cause human disease. Several statistical and geographical modelling studies have estimated and predicted the spread of the H7N9 virus in humans and avian species, and some have identified potential risk factors associated with disease transmission. Additionally, assessment tools have been developed to evaluate the pandemic potential of H7N9 and other influenza viruses. These tools could also hypothetically be used to monitor changes in the pandemic potential of a particular virus over time.