Super-spreaders in infectious diseases

Humans seropositive for Trypanosoma cruzi co-infected with intestinal helminths have higher infectiousness, parasitaemia and Th2-type response in the Argentine Chaco

BACKGROUND

The Gran Chaco ecoregion is a well-known hotspot of several neglected tropical diseases (NTDs) including Chagas disease, soil-transmitted helminthiasis and multiparasitic infections. Interspecific interactions between parasite species can modify host susceptibility, pathogenesis and transmissibility through immunomodulation. Our objective was to test the association between human co-infection with intestinal parasites and host parasitaemia, infectiousness to the vector and immunological profiles in Trypanosoma cruzi-seropositive individuals residing in an endemic region of the Argentine Chaco.

METHODS

We conducted a cross-sectional serological survey for T. cruzi infection along with an intestinal parasite survey in two adjacent rural villages. Each participant was tested for T. cruzi and Strongyloides stercoralis infection by serodiagnosis, and by coprological tests for intestinal parasite detection. Trypanosoma cruzi bloodstream parasite load was determined by quantitative PCR (qPCR), host infectiousness by artificial xenodiagnosis and serum human cytokine levels by flow cytometry.

RESULTS

The seroprevalence for T. cruzi was 16.1% and for S. stercoralis 11.5% (n = 87). We found 25.3% of patients with Enterobius vermicularis. The most frequent protozoan parasites were Blastocystis spp. (39.1%), Giardia lamblia (6.9%) and Cryptosporidium spp. (3.4%). Multiparasitism occurred in 36.8% of the examined patients. Co-infection ranged from 6.9% to 8.1% for T. cruzi-seropositive humans simultaneously infected with at least one protozoan or helminth species, respectively. The relative odds of being positive by qPCR or xenodiagnosis (i.e. infectious) of 28 T. cruzi-seropositive patients was eight times higher in people co-infected with at least one helminth species than in patients with no such co-infection. Trypanosoma cruzi parasite load and host infectiousness were positively associated with helminth co-infection in a multiple regression analysis. Interferon-gamma (IFN-γ) response, measured in relation to interleukin (IL)-4 among humans infected with T. cruzi only, was 1.5-fold higher than for T. cruzi-seropositive patients co-infected with helminths. The median concentration of IL-4 was significantly higher in T. cruzi-seropositive patients with a positive qPCR test than in qPCR-negative patients.

CONCLUSIONS

Our results show a high level of multiparasitism and suggest that co-infection with intestinal helminths increased T. cruzi parasitaemia and upregulated the Th2-type response in the study patients.

Repetition in social contacts: implications in modelling the transmission of respiratory infectious diseases in pre-pandemic and pandemic settings

The spread of viral respiratory infections is intricately linked to human interactions, and this relationship can be characterized and modelled using social contact data. However, many analyses tend to overlook the recurrent nature of these contacts. To bridge this gap, we undertake the task of describing individuals' contact patterns over time by characterizing the interactions made with distinct individuals during a week. Moreover, we gauge the implications of this temporal reconstruction on disease transmission by juxtaposing it with the assumption of random mixing over time. This involves the development of an age-structured individual-based model, using social contact data from a pre-pandemic scenario (the POLYMOD study) and a pandemic setting (the Belgian CoMix study), respectively. We found that accounting for the frequency of contacts impacts the number of new, distinct, contacts, revealing a lower total count than a naive approach, where contact repetition is neglected. As a consequence, failing to account for the repetition of contacts can result in an underestimation of the transmission probability given a contact, potentially leading to inaccurate conclusions when using mathematical models for disease control. We, therefore, underscore the necessity of acknowledging contact repetition when formulating effective public health strategies.

Spatial and temporal transmission dynamics of respiratory syncytial virus in New Zealand before and after the COVID-19 pandemic

Human respiratory syncytial virus (RSV) is a major cause of acute respiratory infection. In 2020, RSV was effectively eliminated from the community in New Zealand due to non-pharmaceutical interventions (NPI) used to control the spread of COVID-19. However, in April 2021, following a brief quarantine-free travel agreement with Australia, there was a large-scale nationwide outbreak of RSV that led to reported cases more than five times higher, and hospitalisations more than three times higher, than the typical seasonal pattern. In this study, we generated 1,471 viral genomes of both RSV-A and RSV-B sampled between 2015 and 2022 from across New Zealand. Using a phylodynamics approach, we used these data to better understand RSV transmission patterns in New Zealand prior to 2020, and how RSV became re-established in the community following the relaxation of COVID-19 restrictions. We found that in 2021, there was a large epidemic of RSV in New Zealand that affected a broader age group range compared to the usual pattern of RSV infections. This epidemic was due to an increase in RSV importations, leading to several large genomic clusters of both RSV-A ON1 and RSV-B BA9 genotypes in New Zealand. However, while a number of importations were detected, there was also a major reduction in RSV genetic diversity compared to pre-pandemic seasonal outbreaks. These genomic clusters were temporally associated with the increase of migration in 2021 due to quarantine-free travel from Australia at the time. The closest genetic relatives to the New Zealand RSV genomes, when sampled, were viral genomes sampled in Australia during a large, off-season summer outbreak several months prior, rather than cryptic lineages that were sustained but not detected in New Zealand. These data reveal the impact of NPI used during the COVID-19 pandemic on other respiratory infections and highlight the important insights that can be gained from viral genomes.

SIR+ models: accounting for interaction-dependent disease susceptibility in the planning of public health interventions

Avoiding physical contact is regarded as one of the safest and most advisable strategies to follow to reduce pathogen spread. The flip side of this approach is that a lack of social interactions may negatively affect other dimensions of health, like induction of immunosuppressive anxiety and depression or preventing interactions of importance with a diversity of microbes, which may be necessary to train our immune system or to maintain its normal levels of activity. These may in turn negatively affect a population's susceptibility to infection and the incidence of severe disease. We suggest that future pandemic modelling may benefit from relying on 'SIR+ models': epidemiological models extended to account for the benefits of social interactions that affect immune resilience. We develop an SIR+ model and discuss which specific interventions may be more effective in balancing the trade-off between minimizing pathogen spread and maximizing other interaction-dependent health benefits. Our SIR+ model reflects the idea that health is not just the mere absence of disease, but rather a state of physical, mental and social well-being that can also be dependent on the same social connections that allow pathogen spread, and the modelling of public health interventions for future pandemics should account for this multidimensionality.

Sudan virus disease super-spreading, Uganda, 2022

BACKGROUND

On 20 September 2022, Uganda declared its fifth Sudan virus disease (SVD) outbreak, culminating in 142 confirmed and 22 probable cases. The reproductive rate (R) of this outbreak was 1.25. We described persons who were exposed to the virus, became infected, and they led to the infection of an unusually high number of cases during the outbreak.

METHODS

In this descriptive cross-sectional study, we defined a super-spreader person (SSP) as any person with real-time polymerase chain reaction (RT-PCR) confirmed SVD linked to the infection of ≥ 13 other persons (10-fold the outbreak R). We reviewed illness narratives for SSPs collected through interviews. Whole-genome sequencing was used to support epidemiologic linkages between cases.

RESULTS

Two SSPs (Patient A, a 33-year-old male, and Patient B, a 26-year-old male) were identified, and linked to the infection of one probable and 50 confirmed secondary cases. Both SSPs lived in the same parish and were likely infected by a single ill healthcare worker in early October while receiving healthcare. Both sought treatment at multiple health facilities, but neither was ever isolated at an Ebola Treatment Unit (ETU). In total, 18 secondary cases (17 confirmed, one probable), including three deaths (17%), were linked to Patient A; 33 secondary cases (all confirmed), including 14 (42%) deaths, were linked to Patient B. Secondary cases linked to Patient A included family members, neighbours, and contacts at health facilities, including healthcare workers. Those linked to Patient B included healthcare workers, friends, and family members who interacted with him throughout his illness, prayed over him while he was nearing death, or exhumed his body. Intensive community engagement and awareness-building were initiated based on narratives collected about patients A and B; 49 (96%) of the secondary cases were isolated in an ETU, a median of three days after onset. Only nine tertiary cases were linked to the 51 secondary cases. Sequencing suggested plausible direct transmission from the SSPs to 37 of 39 secondary cases with sequence data.

CONCLUSION

Extended time in the community while ill, social interactions, cross-district travel for treatment, and religious practices contributed to SVD super-spreading. Intensive community engagement and awareness may have reduced the number of tertiary infections. Intensive follow-up of contacts of case-patients may help reduce the impact of super-spreading events.

Transformation starts at the periphery of networks where pushback is less

Complex systems ranging from societies to ecological communities and power grids may be viewed as networks of connected elements. Such systems can go through critical transitions driven by an avalanche of contagious change. Here we ask, where in a complex network such a systemic shift is most likely to start. Intuitively, a central node seems the most likely source of such change. Indeed, topological studies suggest that central nodes can be the Achilles heel for attacks. We argue that the opposite is true for the class of networks in which all nodes tend to follow the state of their neighbors, a category we call two-way pull networks. In this case, a well-connected central node is an unlikely starting point of a systemic shift due to the buffering effect of connected neighbors. As a result, change is most likely to cascade through the network if it spreads first among relatively poorly connected nodes in the periphery. The probability of such initial spread is highest when the perturbation starts from intermediately connected nodes at the periphery, or more specifically, nodes with intermediate degree and relatively low closeness centrality. Our finding is consistent with empirical observations on social innovation, and may be relevant to topics as different as the sources of originality of art, collapse of financial and ecological networks and the onset of psychiatric disorders.

Film dosimetry for occupant exposure monitoring within Far-UVC installations

Far-UVC radiation between 200 and 230 nm is a promising technology for reducing airborne disease transmission. Previous work with far-UVC lamps has demonstrated the efficacy of far-UVC radiation to inactivate bacteria and viruses while presenting minimal human health hazards. While far-UVC intentionally exposes the occupied space, effectively disinfecting air between occupants, installations must still ensure that occupant eye and skin exposure is within the recommended daily limits. This study examines far-UVC-sensitive films for measuring the dose received by occupants within two real-world far-UVC installations. The film is characterized for accuracy, angular response, wavelength response, and sources of uncertainty in film response, and used to obtain individual exposure doses that account for both the non-uniform irradiance and the unique motion of individuals within the space. Dosimetry results using the films, which account for the time-weighted average exposure of an occupant, ranged from 10% to 49% of the maximum calculated stationary dose based on peak irradiance measurements. Results from this study spotlight the need to incorporate time-weighted average considerations into the design and safety assessment of far-UVC installations to ultimately operate far-UVC technology with its full potential to prevent the spread of potentially fatal infectious diseases.

Caterpillar movement mediates spatially local interactions and determines the relationship between population density and contact

BACKGROUND

While interactions in nature are inherently local, ecological models often assume homogeneity across space, allowing for generalization across systems and greater mathematical tractability. Density-dependent disease models are a prominent example of models that assume homogeneous interactions, leading to the prediction that disease transmission will scale linearly with population density. In this study, we examined how the scale of larval butterfly movement interacts with the resource landscape to influence the relationship between larval contact and population density in the Baltimore checkerspot (Euphydryas phaeton). Our study was inspired by the recent discovery of a viral pathogen that is transmitted horizontally among Baltimore checkerspot larvae.

METHODS

We used multi-year larvae location data across six Baltimore checkerspot populations in the eastern U.S. to test whether larval nests are spatially clustered. We then integrated these spatial data with larval movement data in different resource contexts to investigate whether heterogeneity in spatially local interactions alters the assumed linear relationship between larval nest density and contact. We used Correlated Random Walk (CRW) models and field observations of larval movement behavior to construct Probability Distribution Functions (PDFs) of larval dispersal, and calculated the overlap in these PDFs to estimate conspecific contact within each population.

RESULTS

We found that all populations exhibited significant spatial clustering in their habitat use. Subsequent larval movement rates were influenced by encounters with host plants and larval age, and under many movement scenarios, the scale of predicted larval movement was not sufficient to allow for the "homogeneous mixing" assumed in density dependent disease models. Therefore, relationships between population density and larval contact were typically non-linear. We also found that observed use of available habitat patches led to significantly greater contact than would occur if habitat use were spatially random.

CONCLUSIONS

These findings strongly suggest that incorporating larval movement and spatial variation in larval interactions is critical to modeling disease outcomes in E. phaeton. Epidemiological models that assume a linear relationship between population density and larval contact have the potential to underestimate transmission rates, especially in small populations that are already vulnerable to extinction.

Characterization of genital chlamydia amongst female sex workers in Nairobi, Kenya

Introduction

genital chlamydia, which is caused by diverse Chlamydia trachomatis (C. trachomatis) genotypes, is largely asymptomatic. We aimed to identify C. trachomatis genotypes causing genital chlamydia among female sex workers attending a sex workers outreach program clinic in Nairobi, Kenya.

Methods

this cross-sectional study was conducted between 18th April 2017 and 19th March 2021. Genitourinary complaints from eligible female sex workers were documented using a structured questionnaire. Endocervical swabs were collected for laboratory analysis. C. trachomatis plasmid DNA was extracted, PCR-amplified, and sequenced. Consensus sequences were generated and aligned with reference sequences to determine the C. trachomatis genotypes. Bivariate analysis was used to determine the association between genitourinary complaints and genital chlamydia.

Results

endocervical swabs were collected from a total of 348 participants. Of these, 46 (13.2%) were positive for C. trachomatis. Most (297, 85.3%) of the participants presented with pelvic discharge with or without other symptoms. Fifteen (15, 4.3%) had abdominal pain and 3 (0.9%) had an itchy vulva. There was no statistically significant relationship between clinical presentation and genital chlamydia. Twenty-three samples were successfully sequenced. Each sequence was at least 90% identical to each of the 13 references C. trachomatis genotypes A, B, C, D, E, F, G, Ia, J, L1, L2, L2b and L3.

Conclusion

we found no significant association between individual genitourinary complaints and genital chlamydia infection. The C. trachomatis genotypes circulating amongst female sex workers in Nairobi could be related to genotypes A, B, C, D, E, F, G, Ia, J, L1, L2, L2b, and L3.

A rigorous theoretical and numerical analysis of a nonlinear reaction-diffusion epidemic model pertaining dynamics of COVID-19

The spatial movement of the human population from one region to another and the existence of super-spreaders are the main factors that enhanced the disease incidence. Super-spreaders refer to the individuals having transmitting ability to multiple pathogens. In this article, an epidemic model with spatial and temporal effects is formulated to analyze the impact of some preventing measures of COVID-19. The model is developed using six nonlinear partial differential equations. The infectious individuals are sub-divided into symptomatic, asymptomatic and super-spreader classes. In this study, we focused on the rigorous qualitative analysis of the reaction-diffusion model. The fundamental mathematical properties of the proposed COVID-19 epidemic model such as boundedness, positivity, and invariant region of the problem solution are derived, which ensure the validity of the proposed model. The model equilibria and its stability analysis for both local and global cases have been presented. The normalized sensitivity analysis of the model is carried out in order to observe the crucial factors in the transmission of infection. Furthermore, an efficient numerical scheme is applied to solve the proposed model and detailed simulation are performed. Based on the graphical observation, diffusion in the context of confined public gatherings is observed to significantly inhibit the spread of infection when compared to the absence of diffusion. This is especially important in scenarios where super-spreaders may play a major role in transmission. The impact of some non-pharmaceutical interventions are illustrated graphically with and without diffusion. We believe that the present investigation will be beneficial in understanding the complex dynamics and control of COVID-19 under various non-pharmaceutical interventions.

Urine shedding patterns of pathogenic Leptospira spp. in dairy cows

Pathogenic Leptospira spp. are zoonotic bacteria that infect wild and domestic animals. Humans contract leptospirosis directly through contact with infected animals or indirectly from contaminated water or soil. In mammalian reservoirs, the pathogen can colonize renal tubules for lengthy periods and persistently contaminate the environment through urine. Cattle have been reported to shed several serovars; with Hardjo the most common serovar found in cattle. Without clinical manifestations, the infection can spread within a herd, impairing productivity, and putting workers like farmers, abattoir operators and veterinarians at risk. The dynamics of pathogenic Leptospira shedding was studied in six dairy herds in southern Chile. Various intermittent urine shedding patterns were found, with elimination periods between 79 and 259 days and bacterial loads ranging from 3 × 10⁴ to 4.4 × 10⁴ bacteria/mL. The current study was the first to assess the various urine shedding patterns and loads of pathogenic leptospires shed through urine of naturally-infected dairy cows. In addition, the study suggests that vaccination does not prevent cattle infection, although it influences loads of pathogenic leptospires excreted in urine. Our study provides a great awareness of asymptomatic animal carriers in an endemic area and will contribute to improving disease control and designing better prevention strategies.

Characterization of superspreaders movement in a bidirectional corridor using a social force model

During infectious disease outbreaks, some infected individuals may spread the disease widely and amplify risks in the community. People whose daily activities bring them in close proximity to many others can unknowingly become superspreaders. The use of contact tracking based on social networks, GPS, or mobile tracking data can help to identify superspreaders and break the chain of transmission. We propose a model that aims at providing insight into risk factors of superspreading events. Here, we use a social force model to estimate the superspreading potential of individuals walking in a bidirectional corridor. First, we applied the model to identify parameters that favor exposure to an infectious person in scattered crowds. We find that low walking speed and high body mass both increase the expected number of close exposures. Panic events exacerbate the risks while social distancing reduces both the number and duration of close encounters. Further, in dense crowds, pedestrians interact more and cannot easily maintain the social distance between them. The number of exposures increases with the density of person in the corridor. The study of movements reveals that individuals walking toward the center of the corridor tend to rotate and zigzag more than those walking along the edges, and thus have higher risks of superspreading. The corridor model can be applied to designing risk reduction measures for specific high volume venues, including transit stations, stadiums, and schools.

Modeling the effectiveness of targeting Rift Valley fever virus vaccination using imperfect network information

Livestock movements contribute to the spread of several infectious diseases. Data on livestock movements can therefore be harnessed to guide policy on targeted interventions for controlling infectious livestock diseases, including Rift Valley fever (RVF)-a vaccine-preventable arboviral fever. Detailed livestock movement data are known to be useful for targeting control efforts including vaccination. These data are available in many countries, however, such data are generally lacking in others, including many in East Africa, where multiple RVF outbreaks have been reported in recent years. Available movement data are imperfect, and the impact of this uncertainty in the utility of movement data on informing targeting of vaccination is not fully understood. Here, we used a network simulation model to describe the spread of RVF within and between 398 wards in northern Tanzania connected by cattle movements, on which we evaluated the impact of targeting vaccination using imperfect movement data. We show that pre-emptive vaccination guided by only market movement permit data could prevent large outbreaks. Targeted control (either by the risk of RVF introduction or onward transmission) at any level of imperfect movement information is preferred over random vaccination, and any improvement in information reliability is advantageous to their effectiveness. Our modeling approach demonstrates how targeted interventions can be effectively used to inform animal and public health policies for disease control planning. This is particularly valuable in settings where detailed data on livestock movements are either unavailable or imperfect due to resource limitations in data collection, as well as challenges associated with poor compliance.

Analysis of contact tracing data showed contribution of asymptomatic and non-severe infections to the maintenance of SARS-CoV-2 transmission in Senegal

During the COVID-19 pandemic in Senegal, contact tracing was done to identify transmission clusters, their analysis allowed to understand their dynamics and evolution. In this study, we used information from the surveillance data and phone interviews to construct, represent and analyze COVID-19 transmission clusters from March 2, 2020, to May 31, 2021. In total, 114,040 samples were tested and 2153 transmission clusters identified. A maximum of 7 generations of secondary infections were noted. Clusters had an average of 29.58 members and 7.63 infected among them; their average duration was 27.95 days. Most of the clusters (77.3%) are concentrated in Dakar, capital city of Senegal. The 29 cases identified as super-spreaders, i.e., the indexes that had the most positive contacts, showed few symptoms or were asymptomatic. Deepest transmission clusters are those with the highest percentage of asymptomatic members. The correlation between proportion of asymptomatic and degree of transmission clusters showed that asymptomatic strongly contributed to the continuity of transmission within clusters. During this pandemic, all the efforts towards epidemiological investigations, active case-contact detection, allowed to identify in a short delay growing clusters and help response teams to mitigate the spread of the disease.

Physical distancing versus testing with self-isolation for controlling an emerging epidemic

Two distinct strategies for controlling an emerging epidemic are physical distancing and regular testing with self-isolation. These strategies are especially important before effective vaccines or treatments become widely available. The testing strategy has been promoted frequently but used less often than physical distancing to mitigate COVID-19. We compared the performance of these strategies in an integrated epidemiological and economic model that includes a simple representation of transmission by "superspreading," wherein a relatively small fraction of infected individuals cause a large share of infections. We examined the economic benefits of distancing and testing over a wide range of conditions, including variations in the transmissibility and lethality of the disease meant to encompass the most prominent variants of COVID-19 encountered so far. In a head-to-head comparison using our primary parameter values, both with and without superspreading and a declining marginal value of mortality risk reductions, an optimized testing strategy outperformed an optimized distancing strategy. In a Monte Carlo uncertainty analysis, an optimized policy that combined the two strategies performed better than either one alone in more than 25% of random parameter draws. Insofar as diagnostic tests are sensitive to viral loads, and individuals with high viral loads are more likely to contribute to superspreading events, superspreading enhances the relative performance of testing over distancing in our model. Both strategies performed best at moderate levels of transmissibility, somewhat lower than the transmissibility of the ancestral strain of SARS-CoV-2.

Positive feedback loops exacerbate the influence of superspreaders in disease transmission

Superspreaders are recognized as being important drivers of disease spread. However, models to date have assumed random occurrence of superspreaders, irrespective of whom they were infected by. Evidence suggests though that those individuals infected by superspreaders may be more likely to become superspreaders themselves. Here, we begin to explore, theoretically, the effects of such a positive feedback loop on (1) the final epidemic size, (2) the herd immunity threshold, (3) the basic reproduction number, R₀, and (4) the peak prevalence of superspreaders, using a generic model for a hypothetical acute viral infection and illustrative parameter values. We show that positive feedback loops can have a profound effect on our chosen epidemic outcomes, even when the transmission advantage of superspreaders is moderate, and despite peak prevalence of superspreaders remaining low. We argue that positive superspreader feedback loops in different infectious diseases, including SARS-CoV-2, should be investigated further, both theoretically and empirically.

Heterogeneity is a key factor describing the initial outbreak of COVID-19

Assessing the transmission potential of emerging infectious diseases, such as COVID-19, is crucial for implementing prompt and effective intervention policies. The basic reproduction number is widely used to measure the severity of the early stages of disease outbreaks. The basic reproduction number of standard ordinary differential equation models is computed for homogeneous contact patterns; however, realistic contact patterns are far from homogeneous, specifically during the early stages of disease transmission. Heterogeneity of contact patterns can lead to superspreading events that show a significantly high level of heterogeneity in generating secondary infections. This is primarily due to the large variance in the contact patterns of complex human behaviours. Hence, in this work, we investigate the impacts of heterogeneity in contact patterns on the basic reproduction number by developing two distinct model frameworks: 1) an SEIR-Erlang ordinary differential equation model and 2) an SEIR stochastic agent-based model. Furthermore, we estimated the transmission probability of both models in the context of COVID-19 in South Korea. Our results highlighted the importance of heterogeneity in contact patterns and indicated that there should be more information than one quantity (the basic reproduction number as the mean quantity), such as a degree-specific basic reproduction number in the distributional sense when the contact pattern is highly heterogeneous.

Identifying Potential Super-Spreaders and Disease Transmission Hotspots Using White-Tailed Deer Scraping Networks

White-tailed deer (Odocoileus virginianus, WTD) spread communicable diseases such the zoonotic coronavirus SARS-CoV-2, which is a major public health concern, and chronic wasting disease (CWD), a fatal, highly contagious prion disease occurring in cervids. Currently, it is not well understood how WTD are spreading these diseases. In this paper, we speculate that "super-spreaders" mediate disease transmission via direct social interactions and indirectly via body fluids exchanged at scrape sites. Super-spreaders are infected individuals that infect more contacts than other infectious individuals within a population. In this study, we used network analysis from scrape visitation data to identify potential super-spreaders among multiple communities of a rural WTD herd. We combined local network communities to form a large region-wide social network consisting of 96 male WTD. Analysis of WTD bachelor groups and random network modeling demonstrated that scraping networks depict real social networks, allowing detection of direct and indirect contacts, which could spread diseases. Using this regional network, we model three major types of potential super-spreaders of communicable disease: in-degree, out-degree, and betweenness potential super-spreaders. We found out-degree and betweenness potential super-spreaders to be critical for disease transmission across multiple communities. Analysis of age structure revealed that potential super-spreaders were mostly young males, less than 2.5 years of age. We also used social network analysis to measure the outbreak potential across the landscape using a new technique to locate disease transmission hotspots. To model indirect transmission risk, we developed the first scrape-to-scrape network model demonstrating connectivity of scrape sites. Comparing scrape betweenness scores allowed us to locate high-risk transmission crossroads between communities. We also monitored predator activity, hunting activity, and hunter harvests to better understand how predation influences social networks and potential disease transmission. We found that predator activity significantly influenced the age structure of scraping communities. We assessed disease-management strategies by social-network modeling using hunter harvests or removal of potential super-spreaders, which fragmented WTD social networks reducing the potential spread of disease. Overall, this study demonstrates a model capable of predicting potential super-spreaders of diseases, outlines methods to locate transmission hotspots and community crossroads, and provides new insight for disease management and outbreak prevention strategies.

Targeted preventive vaccination campaigns to reduce Ebola outbreaks: An individual-based modeling study

INTRODUCTION

Nonpharmaceutical interventions (NPI) and ring vaccination (i.e., vaccination that primarily targets contacts and contacts of contacts of Ebola cases) are currently used to reduce the spread of Ebola during outbreaks. Because these measures are typically initiated after an outbreak is declared, they are limited by real-time implementation challenges. Preventive vaccination may provide a complementary option to help protect communities against unpredictable outbreaks. This study aimed to assess the impact of preventive vaccination strategies when implemented in conjunction with NPI and ring vaccination.

METHODS

A spatial-explicit, individual-based model (IBM) that accounts for heterogeneity of human contact, human movement, and timing of interventions was built to represent Ebola transmission in the Democratic Republic of the Congo. Simulated preventive vaccination strategies targeted healthcare workers (HCW), frontline workers (FW), and the general population (GP) with varying levels of coverage (lower coverage: 30% of HCW/FW, 5% of GP; higher coverage: 60% of HCW/FW, 10% of GP) and efficacy (lower efficacy: 60%; higher efficacy: 90%).

RESULTS

The IBM estimated that the addition of preventive vaccination for HCW reduced cases, hospitalizations, and deaths by ∼11 % to ∼25 % compared with NPI + ring vaccination alone. Including HCW and FW in the preventive vaccination campaign yielded ∼14 % to ∼38 % improvements in epidemic outcomes. Further including the GP yielded the greatest improvements, with ∼21 % to ∼52 % reductions in epidemic outcomes compared with NPI + ring vaccination alone. In a scenario without ring vaccination, preventive vaccination reduced cases, hospitalizations, and deaths by ∼28 % to ∼59 % compared with NPI alone. In all scenarios, preventive vaccination reduced Ebola transmission particularly during the initial phases of the epidemic, resulting in flatter epidemic curves.

CONCLUSIONS

The IBM showed that preventive vaccination may reduce Ebola cases, hospitalizations, and deaths, thus safeguarding the healthcare system and providing more time to implement additional interventions during an outbreak.

Evaluation of risk factors associated with SARS-CoV-2 transmission

OBJECTIVE

Coronavirus disease 2019 (COVID-19) has caused high morbidity and mortality worldwide. Since there is not enough evidence of risk factors of SARS-CoV-2 transmission, this study aimed to evaluate them.

METHODS

This survey-based study was conducted across 66 countries from May to November 2020 among suspected and confirmed individuals with COVID-19. The stepwise AIC method was utilized to determine the optimal multivariable logistic regression to explore predictive factors of SARS-CoV-2 transmission.

RESULTS

Among 2372 respondents who participated in the study, there were 1172 valid responses. The profession of non-healthcare-worker (OR: 1.77, 95%CI: 1.04-3.00, p = .032), history of SARS-CoV or MERS-CoV infection (OR: 4.78, 95%CI: 2.34-9.63, p < .001), higher frequency of contact with colleagues (OR: 1.17, 95%CI: 1.01-1.37, p = .041), and habit of hugging when greeting (OR: 1.25, 95%CI: 1.00-1.56, p = .049) were associated with an increased risk of contracting COVID-19. Current smokers had a lower likelihood of having COVID-19 compared to former smokers (OR: 5.41, 95%CI: 1.93-17.49, p = .002) or non-smokers (OR: 3.69, 95%CI: 1.48-11.11, p = .01).

CONCLUSIONS

Our study suggests several risk factors for SARS-CoV-2 transmission including the profession of non-healthcare workers, history of other coronavirus infections, frequent close contact with colleagues, the habit of hugging when greeting, and smoking status.

Monitoring real-time transmission heterogeneity from incidence data

The transmission heterogeneity of an epidemic is associated with a complex mixture of host, pathogen and environmental factors. And it may indicate superspreading events to reduce the efficiency of population-level control measures and to sustain the epidemic over a larger scale and a longer duration. Methods have been proposed to identify significant transmission heterogeneity in historic epidemics based on several data sources, such as contact history, viral genomes and spatial information, which may not be available, and more importantly ignore the temporal trend of transmission heterogeneity. Here we attempted to establish a convenient method to estimate real-time heterogeneity over an epidemic. Within the branching process framework, we introduced an instant-individualheterogenous infectiousness model to jointly characterize the variation in infectiousness both between individuals and among different times. With this model, we could simultaneously estimate the transmission heterogeneity and the reproduction number from incidence time series. We validated the model with data of both simulated and real outbreaks. Our estimates of the overall and real-time heterogeneities of the six epidemics were consistent with those presented in the literature. Additionally, our model is robust to the ubiquitous bias of under-reporting and misspecification of serial interval. By analyzing recent data from South Africa, we found evidence that the Omicron might be of more significant transmission heterogeneity than Delta. Our model based on incidence data was proved to be reliable in estimating the real-time transmission heterogeneity.

A topology-based evaluation of resilience on urban road networks against epidemic spread: Implications for COVID-19 responses

Road closure is an effective measure to reduce mobility and prevent the spread of an epidemic in severe public health crises. For instance, during the peak waves of the global COVID-19 pandemic, many countries implemented road closure policies, such as the traffic-calming strategy in the UK. However, it is still not clear how such road closures, if used as a response to different modes of epidemic spreading, affect the resilient performance of large-scale road networks in terms of their efficiency and overall accessibility. In this paper, we propose a simulation-based approach to theoretically investigate two types of spreading mechanisms and evaluate the effectiveness of both static and dynamic response scenarios, including the sporadic epidemic spreading based on network topologies and trajectory-based spreading caused by superspreaders in megacities. The results showed that (1) the road network demonstrates comparatively worse resilient behavior under the trajectory-based spreading mode; (2) the road density and centrality order, as well as the network's regional geographical characteristics, can substantially alter the level of impacts and introduce heterogeneity into the recovery processes; and (3) the resilience lost under static recovery and dynamic recovery scenarios is 8.6 and 6.9%, respectively, which demonstrates the necessity of a dynamic response and the importance of making a systematic and strategic recovery plan. Policy and managerial implications are also discussed. This paper provides new insights for better managing the resilience of urban road networks against public health crises in the post-COVID era.

Characterizing respiratory aerosol emissions during sustained phonation

OBJECTIVE

To elucidate the role of phonation frequency (i.e., pitch) and intensity of speech on respiratory aerosol emissions during sustained phonations.

METHODS

Respiratory aerosol emissions are measured in 40 (24 males and 16 females) healthy, non-trained singers phonating the phoneme /a/ at seven specific frequencies at varying vocal intensity levels.

RESULTS

Increasing frequency of phonation was positively correlated with particle production (r = 0.28, p < 0.001). Particle production rate was also positively correlated (r = 0.37, p < 0.001) with the vocal intensity of phonation, confirming previously reported findings. The primary mode (particle diameter ~0.6 μm) and width of the particle number size distribution were independent of frequency and vocal intensity. Regression models of the particle production rate using frequency, vocal intensity, and the individual subject as predictor variables only produced goodness of fit of adjusted R² = 40% (p < 0.001). Finally, it is proposed that superemitters be defined as statistical outliers, which resulted in the identification of one superemitter in the sample of 40 participants.

SIGNIFICANCE

The results suggest there remain unexplored effects (e.g., biomechanical, environmental, behavioral, etc.) that contribute to the high variability in respiratory particle production rates, which ranged from 0.2 particles/s to 142 particles/s across all trials. This is evidenced as well by changes in the distribution of participant particle production that transitions to a more bimodal distribution (second mode at particle diameter ~2 μm) at higher frequencies and vocal intensity levels.

A Hybrid Epidemic Model to Explore Stochasticity in COVID-19 Dynamics

The dynamic nature of the COVID-19 pandemic has demanded a public health response that is constantly evolving due to the novelty of the virus. Many jurisdictions in the USA, Canada, and across the world have adopted social distancing and recommended the use of face masks. Considering these measures, it is prudent to understand the contributions of subpopulations—such as “silent spreaders”—to disease transmission dynamics in order to inform public health strategies in a jurisdiction-dependent manner. Additionally, we and others have shown that demographic and environmental stochasticity in transmission rates can play an important role in shaping disease dynamics. Here, we create a model for the COVID-19 pandemic by including two classes of individuals: silent spreaders, who either never experience a symptomatic phase or remain undetected throughout their disease course; and symptomatic spreaders, who experience symptoms and are detected. We fit the model to real-time COVID-19 confirmed cases and deaths to derive the transmission rates, death rates, and other relevant parameters for multiple phases of outbreaks in British Columbia (BC), Canada. We determine the extent to which SilS contributed to BC’s early wave of disease transmission as well as the impact of public health interventions on reducing transmission from both SilS and SymS. To do this, we validate our model against an existing COVID-19 parameterized framework and then fit our model to clinical data to estimate key parameter values for different stages of BC’s disease dynamics. We then use these parameters to construct a hybrid stochastic model that leverages the strengths of both a time-nonhomogeneous discrete process and a stochastic differential equation model. By combining these previously established approaches, we explore the impact of demographic and environmental variability on disease dynamics by simulating various scenarios in which a COVID-19 outbreak is initiated. Our results demonstrate that variability in disease transmission rate impacts the probability and severity of COVID-19 outbreaks differently in high- versus low-transmission scenarios.

Bronchial epithelia from adults and children: SARS-CoV-2 spread via syncytia formation and type III interferon infectivity restriction

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections initiate in the bronchi of the upper respiratory tract and are able to disseminate to the lower respiratory tract, where infections can cause an acute respiratory distress syndrome with a high degree of mortality in elderly patients. We used reconstituted primary bronchial epithelia from adult and child donors to follow the SARS-CoV-2 infection dynamics. We show that, in epithelia from adult donors, infections initiate in multiciliated cells and spread within 24 to 48 h throughout the whole epithelia. Syncytia formed of ciliated and basal cells appeared at the apical side of the epithelia within 3 to 4 d and were released into the apical lumen, where they contributed to the transmittable virus dose. A small number of reconstituted epithelia were intrinsically more resistant to virus infection, limiting virus spread to different degrees. This phenotype was more frequent in epithelia derived from children versus adults and correlated with an accelerated release of type III interferon. Treatment of permissive adult epithelia with exogenous type III interferon restricted infection, while type III interferon gene knockout promoted infection. Furthermore, a transcript analysis revealed that the inflammatory response was specifically attenuated in children. Taken together, our findings suggest that apical syncytia formation is an underappreciated source of virus propagation for tissue or environmental dissemination, whereas a robust type III interferon response such as commonly seen in young donors restricted SARS-CoV-2 infection. Thus, the combination of interferon restriction and attenuated inflammatory response in children might explain the epidemiological observation of age-related susceptibility to COVID-19.

Seasonal dynamics of tick burden and associated Borrelia burgdorferi s.l. and Borrelia miyamotoi infections in rodents in a Dutch forest ecosystem

Ixodes ricinus ticks transmit Borrelia burgdorferi sensu lato (s.l.) as well as Borrelia miyamotoi. Larvae become infected when feeding on infected rodents, with horizontal transmission of B. burgdorferi and horizontal and vertical transmission of B. miyamotoi. We studied seasonal dynamics of infection rates of I. ricinus and their rodent hosts, and hence transmission risk of these two distinctly different Borrelia species. Rodents were live-trapped and inspected for ticks from May to November in 2013 and 2014 in a forest in The Netherlands. Trapped rodents were temporarily housed in the laboratory and detached ticks were collected. Borrelia infections were determined from the trapped rodents and collected ticks. Borrelia burgdorferi s.l. and B. miyamotoi were found in ticks as well as in rodents. Rodent density was higher in 2014, whereas tick burden as well as the Borrelia infection rates in rodents were higher in 2013. The density of B. miyamotoi-infected nymphs did not differ between the years. Tick burdens were higher on Apodemus sylvaticus than on Myodes glareolus, and higher on males than on females. Borrelia-infection rate of rodents varied strongly seasonally, peaking in summer. As the larval tick burden also peaked in summer, the generation of infected nymphs was highest in summer. We conclude that the heterogeneity of environmental and host-specific factors affects the seasonal transmission of Borrelia spp., and that these effects act more strongly on horizontally transmitted B. burgdorferi spp. than on the vertically transmitted B. miyamotoi.

Bacterial-Viral Interactions: A Factor That Facilitates Transmission Heterogeneities

The transmission of infectious diseases is characterized by heterogeneities that are shaped by the host, the pathogen, and the environment. Extreme forms of these heterogeneities are called super-spreading events. Transmission heterogeneities are usually identified retrospectively, but their contribution to the dynamics of outbreaks makes the ability to predict them valuable for science, medicine, and public health. Previous studies identified several factors that facilitate super-spreading; one of them is the interaction between bacteria and viruses within a host. The heightened dispersal of bacteria colonizing the nasal cavity during an upper respiratory viral infection, and the increased shedding of HIV-1 from the urogenital tract during a sexually transmitted bacterial infection, are among the most extensively studied examples of transmission heterogeneities that result from bacterial-viral interactions. Interrogating these transmission heterogeneities, and elucidating the underlying cellular and molecular mechanisms, are part of much-needed efforts to guide public health interventions, in areas that range from predicting or controlling the population transmission of respiratory pathogens, to limiting the spread of sexually transmitted infections, and tailoring vaccination initiatives with live attenuated vaccines.

System dynamic modelling of healthcare associated influenza -a tool for infection control

BACKGROUND

The transmission dynamics of influenza virus within healthcare settings are not fully understood. Capturing the interplay between host, viral and environmental factors is difficult using conventional research methods. Instead, system dynamic modelling may be used to illustrate the complex scenarios including non-linear relationships and multiple interactions which occur within hospitals during a seasonal influenza epidemic. We developed such a model intended as a support for health-care providers in identifying potentially effective control strategies to prevent influenza transmission.

METHODS

By using computer simulation software, we constructed a system dynamic model to illustrate transmission dynamics within a large acute-care hospital. We used local real-world clinical and epidemiological data collected during the season 2016/17, as well as data from the national surveillance programs and relevant publications to form the basic structure of the model. Multiple stepwise simulations were performed to identify the relative effectiveness of various control strategies and to produce estimates of the accumulated number of healthcare-associated influenza cases per season.

RESULTS

Scenarios regarding the number of patients exposed for influenza virus by shared room and the extent of antiviral prophylaxis and treatment were investigated in relation to estimations of influenza vaccine coverage, vaccine effectiveness and inflow of patients with influenza. In total, 680 simulations were performed, of which each one resulted in an estimated number per season. The most effective preventive measure identified by our model was administration of antiviral prophylaxis to exposed patients followed by reducing the number of patients receiving care in shared rooms.

CONCLUSIONS

This study presents an system dynamic model that can be used to capture the complex dynamics of in-hospital transmission of viral infections and identify potentially effective interventions to prevent healthcare-associated influenza infections. Our simulations identified antiviral prophylaxis as the most effective way to control in-hospital influenza transmission.

SARS-CoV-2 Droplet and Airborne Transmission Heterogeneity

The spread dynamics of the SARS-CoV-2 virus have not yet been fully understood after two years of the pandemic. The virus's global spread represented a unique scenario for advancing infectious disease research. Consequently, mechanistic epidemiological theories were quickly dismissed, and more attention was paid to other approaches that considered heterogeneity in the spread. One of the most critical advances in aerial pathogens transmission was the global acceptance of the airborne model, where the airway is presented as the epicenter of the spread of the disease. Although the aerodynamics and persistence of the SARS-CoV-2 virus in the air have been extensively studied, the actual probability of contagion is still unknown. In this work, the individual heterogeneity in the transmission of 22 patients infected with COVID-19 was analyzed by close contact (cough samples) and air (environmental samples). Viral RNA was detected in 2/19 cough samples from patient subgroups, with a mean Ct (Cycle Threshold in Quantitative Polymerase Chain Reaction analysis) of 25.7 ± 7.0. Nevertheless, viral RNA was only detected in air samples from 1/8 patients, with an average Ct of 25.0 ± 4.0. Viral load in cough samples ranged from 7.3 × 105 to 8.7 × 108 copies/mL among patients, while concentrations between 1.1-4.8 copies/m3 were found in air, consistent with other reports in the literature. In patients undergoing follow-up, no viral load was found (neither in coughs nor in the air) after the third day of symptoms, which could help define quarantine periods in infected individuals. In addition, it was found that the patient's Ct should not be considered an indicator of infectiousness, since it could not be correlated with the viral load disseminated. The results of this work are in line with proposed hypotheses of superspreaders, which can attribute part of the heterogeneity of the spread to the oversized emission of a small percentage of infected people.

Mucins Inhibit Coronavirus Infection in a Glycan-Dependent Manner

Mucins are a diverse and heterogeneous family of glycoproteins that comprise the bulk of mucus and the epithelial glycocalyx. Mucins are intimately involved in viral transmission. Mucin and virus laden particles can be expelled from the mouth and nose to later infect others. Viruses must also penetrate the mucus layer before cell entry and replication. The role of mucins and their molecular structure have not been well-characterized in coronavirus transmission studies. Laboratory studies predicting high rates of fomite transmission have not translated to real-world infections, and mucins may be one culprit. Here, we probed both surface and direct contact transmission scenarios for their dependence on mucins and their structure. We utilized disease-causing, bovine-derived, human coronavirus OC43. We found that bovine mucins could inhibit the infection of live cells in a concentration- and glycan-dependent manner. The effects were observed in both mock fomite and direct contact transmission experiments and were not dependent upon surface material or time-on-surface. However, the effects were abrogated by removal of the glycans or in a cross-species infection scenario where bovine mucin could not inhibit the infection of a murine coronavirus. Together, our data indicate that the mucin molecular structure plays a complex and important role in host defense.

A sampling-guided unsupervised learning method to capture percolation in complex networks

The use of machine learning methods in classical and quantum systems has led to novel techniques to classify ordered and disordered phases, as well as uncover transition points in critical phenomena. Efforts to extend these methods to dynamical processes in complex networks is a field of active research. Network-percolation, a measure of resilience and robustness to structural failures, as well as a proxy for spreading processes, has numerous applications in social, technological, and infrastructural systems. A particular challenge is to identify the existence of a percolation cluster in a network in the face of noisy data. Here, we consider bond-percolation, and introduce a sampling approach that leverages the core-periphery structure of such networks at a microscopic scale, using onion decomposition, a refined version of the k-core. By selecting subsets of nodes in a particular layer of the onion spectrum that follow similar trajectories in the percolation process, percolating phases can be distinguished from non-percolating ones through an unsupervised clustering method. Accuracy in the initial step is essential for extracting samples with information-rich content, that are subsequently used to predict the critical transition point through the confusion scheme, a recently introduced learning method. The method circumvents the difficulty of missing data or noisy measurements, as it allows for sampling nodes from both the core and periphery, as well as intermediate layers. We validate the effectiveness of our sampling strategy on a spectrum of synthetic network topologies, as well as on two real-word case studies: the integration time of the US domestic airport network, and the identification of the epidemic cluster of COVID-19 outbreaks in three major US states. The method proposed here allows for identifying phase transitions in empirical time-varying networks.

Differential gene expression reveals host factors for viral shedding variation in mallards (Anas platyrhynchos) infected with low-pathogenic avian influenza virus

Intraspecific variation in pathogen shedding impacts disease transmission dynamics; therefore, understanding the host factors associated with individual variation in pathogen shedding is key to controlling and preventing outbreaks. In this study, ileum and bursa of Fabricius tissues of wild-bred mallards (Anas platyrhynchos) infected with low-pathogenic avian influenza (LPAIV) were evaluated at various post-infection time points to determine genetic host factors associated with intraspecific variation in viral shedding. By analysing transcriptome sequencing data (RNA-seq), we found that LPAIV-infected wild-bred mallards do not exhibit differential gene expression compared to uninfected birds, but that gene expression was associated with cloacal viral shedding quantity early in the infection. In both tissues, immune gene expression was higher in high/moderate shedding birds compared to low shedding birds, and significant positive relationships with viral shedding were observed. In the ileum, expression for host genes involved in viral cell entry was lower in low shedders compared to moderate shedders at 1 day post-infection (DPI), and expression for host genes promoting viral replication was higher in high shedders compared to low shedders at 2 DPI. Our findings indicate that viral shedding is a key factor for gene expression differences in LPAIV-infected wild-bred mallards, and the genes identified in this study could be important for understanding the molecular mechanisms driving intraspecific variation in pathogen shedding.

The Epidemiology of Sudden Oak Death Disease Caused by Phytophthora ramorum in a Mixed Bay Laurel-Oak Woodland Provides Important Clues for Disease Management

Epidemiological models are important for the understanding of disease progression in plants and for the design of control strategies. Phytophthora ramorum, the pathogen responsible for the disease known as Sudden Oak Death, causes lethal infection on several oaks but relies on California bay laurels for transmission. Here, repeated surveys of bay laurels and oaks indicated that bay laurel disease incidence was positively correlated with rainfall, bay laurel density, and an eastern aspect, and negatively correlated with bay laurel basal area. Oak infection only occurred in years when rainfall was higher than the 30-year average, and although infection rates were greater among larger trees, mortality was greater among smaller trees. Additionally, larger oaks closer to infected bay laurels exhibited greater infection rates. Disease incidence differed among sites, and only a fraction of bay laurels were disease superspreaders, while even fewer individuals were refugial trees harboring active infections during dry periods. Based on this study, reducing bay laurel density in denser stands and the number of superspreaders or refugial trees in less dense stands may reduce disease incidence. However, the selective removal of bay laurel trees 0–10 m from oaks is likely to be more effective in preventing infection of specific oaks.

Estimating the effect of non-pharmaceutical interventions to mitigate COVID-19 spread in Saudi Arabia

BACKGROUND

The Kingdom of Saudi Arabia (KSA) quickly controlled the spread of SARS-CoV-2 by implementing several non-pharmaceutical interventions (NPIs), including suspension of international and national travel, local curfews, closing public spaces (i.e., schools and universities, malls and shops), and limiting religious gatherings. The KSA also mandated all citizens to respect physical distancing and to wear face masks. However, after relaxing some restrictions during June 2020, the KSA is now planning a strategy that could allow resuming in-person education and international travel. The aim of our study was to evaluate the effect of NPIs on the spread of the COVID-19 and test strategies to open schools and resume international travel.

METHODS

We built a spatial-explicit individual-based model to represent the whole KSA population (IBM-KSA). The IBM-KSA was parameterized using country demographic, remote sensing, and epidemiological data. A social network was created to represent contact heterogeneity and interaction among age groups of the population. The IBM-KSA also simulated the movement of people across the country based on a gravity model. We used the IBM-KSA to evaluate the effect of different NPIs adopted by the KSA (physical distancing, mask-wearing, and contact tracing) and to forecast the impact of strategies to open schools and resume international travels.

RESULTS

The IBM-KSA results scenarios showed the high effectiveness of mask-wearing, physical distancing, and contact tracing in controlling the spread of the disease. Without NPIs, the KSA could have reported 4,824,065 (95% CI: 3,673,775-6,335,423) cases by June 2021. The IBM-KSA showed that mandatory mask-wearing and physical distancing saved 39,452 lives (95% CI: 26,641-44,494). In-person education without personal protection during teaching would have resulted in a high surge of COVID-19 cases. Compared to scenarios with no personal protection, enforcing mask-wearing and physical distancing in schools reduced cases, hospitalizations, and deaths by 25% and 50%, when adherence to these NPIs was set to 50% and 70%, respectively. The IBM-KSA also showed that a quarantine imposed on international travelers reduced the probability of outbreaks in the country.

CONCLUSIONS

This study showed that the interventions adopted by the KSA were able to control the spread of SARS-CoV-2 in the absence of a vaccine. In-person education should be resumed only if NPIs could be applied in schools and universities. International travel can be resumed but with strict quarantine rules. The KSA needs to keep strict NPIs in place until a high fraction of the population is vaccinated in order to reduce hospitalizations and deaths.

Reconstruction of the transmission dynamics of the first COVID-19 epidemic wave in Thailand

Thailand was the first country reporting the first Coronavirus disease 2019 (COVID-19) infected individual outside mainland China. Here we delineated the course of the COVID-19 outbreak together with the timeline of the control measures and public health policies employed by the Thai government during the first wave of the COVID-19 outbreak in Thailand. Based on the comprehensive epidemiological data, we reconstructed the dynamics of COVID-19 transmission in Thailand using a stochastic modeling approach. Our stochastic model incorporated the effects of individual heterogeneity in infectiousness on disease transmission, which allows us to capture relevant features of superspreading events. We found that our model could accurately capture the transmission dynamics of the first COVID-19 epidemic wave in Thailand. The model predicted that at the end of the first wave, the number of cumulative confirmed cases was 3091 (95%CI: 2782-3400). We also estimated the time-varying reproduction number (R_t) during the first epidemic wave. We found that after implementing the nationwide interventions, the R_t in Thailand decreased from the peak value of 5.67 to a value below one in less than one month, indicating that the control measures employed by the Thai government during the first COVID-19 epidemic wave were effective. Finally, the effects of transmission heterogeneity and control measures on the likelihood of outbreak extinction were also investigated.

Prioritizing high-contact occupations raises effectiveness of vaccination campaigns

A twenty-year-old idea from network science is that vaccination campaigns would be more effective if high-contact individuals were preferentially targeted. Implementation is impeded by the ethical and practical problem of differentiating vaccine access based on a personal characteristic that is hard-to-measure and private. Here, we propose the use of occupational category as a proxy for connectedness in a contact network. Using survey data on occupation-specific contact frequencies, we calibrate a model of disease propagation in populations undergoing varying vaccination campaigns. We find that vaccination campaigns that prioritize high-contact occupational groups achieve similar infection levels with half the number of vaccines, while also reducing and delaying peaks. The paper thus identifies a concrete, operational strategy for dramatically improving vaccination efficiency in ongoing pandemics.

A Simulation Study on Spread of Disease and Control Measures in Closed Population Using ABM

An infectious disease can cause a detrimental effect on national security. A group such as the military called a “closed population”, which is a subset of the general population but has many distinct characteristics, must survive even in the event of a pandemic. Hence, it requires its own distinct solution during a pandemic. In this study, we investigate a simulation analysis for implementing an agent-based model that reflects the characteristics of agents and the environment in a closed population and finds effective control measures for making the closed population functional in the course of disease spreading.

TNet: Transmission Network Inference Using Within-Host Strain Diversity and its Application to Geographical Tracking of COVID-19 Spread

The inference of disease transmission networks is an important problem in epidemiology. One popular approach for building transmission networks is to reconstruct a phylogenetic tree using sequences from disease strains sampled from infected hosts and infer transmissions based on this tree. However, most existing phylogenetic approaches for transmission network inference are highly computationally intensive and cannot take within-host strain diversity into account. Here, we introduce a new phylogenetic approach for inferring transmission networks, TNet, that addresses these limitations. TNet uses multiple strain sequences from each sampled host to infer transmissions and is simpler and more accurate than existing approaches. Furthermore, TNet is highly scalable and able to distinguish between ambiguous and unambiguous transmission inferences. We evaluated TNet on a large collection of 560 simulated transmission networks of various sizes and diverse host, sequence, and transmission characteristics, as well as on 10 real transmission datasets with known transmission histories. Our results show that TNet outperforms two other recently developed methods, phyloscanner and SharpTNI, that also consider within-host strain diversity. We also applied TNet to a large collection of SARS-CoV-2 genomes sampled from infected individuals in many countries around the world, demonstrating how our inference framework can be adapted to accurately infer geographical transmission networks. TNet is freely available from https://compbio.engr.uconn.edu/software/TNet/.

Comparing variability in diagnosis of upper respiratory tract infections in patients using syndromic, next generation sequencing, and PCR-based methods

Early and accurate diagnosis of respiratory pathogens and associated outbreaks can allow for the control of spread, epidemiological modeling, targeted treatment, and decision making-as is evident with the current COVID-19 pandemic. Many respiratory infections share common symptoms, making them difficult to diagnose using only syndromic presentation. Yet, with delays in getting reference laboratory tests and limited availability and poor sensitivity of point-of-care tests, syndromic diagnosis is the most-relied upon method in clinical practice today. Here, we examine the variability in diagnostic identification of respiratory infections during the annual infection cycle in northern New Mexico, by comparing syndromic diagnostics with polymerase chain reaction (PCR) and sequencing-based methods, with the goal of assessing gaps in our current ability to identify respiratory pathogens. Of 97 individuals that presented with symptoms of respiratory infection, only 23 were positive for at least one RNA virus, as confirmed by sequencing. Whereas influenza virus (n = 7) was expected during this infection cycle, we also observed coronavirus (n = 7), respiratory syncytial virus (n = 8), parainfluenza virus (n = 4), and human metapneumovirus (n = 1) in individuals with respiratory infection symptoms. Four patients were coinfected with two viruses. In 21 individuals that tested positive using PCR, RNA sequencing completely matched in only 12 (57%) of these individuals. Few individuals (37.1%) were diagnosed to have an upper respiratory tract infection or viral syndrome by syndromic diagnostics, and the type of virus could only be distinguished in one patient. Thus, current syndromic diagnostic approaches fail to accurately identify respiratory pathogens associated with infection and are not suited to capture emerging threats in an accurate fashion. We conclude there is a critical and urgent need for layered agnostic diagnostics to track known and unknown pathogens at the point of care to control future outbreaks.

The Genomic Physics of COVID-19 Pathogenesis and Spread

Coronavirus disease (COVID-19) spreads mainly through close contact of infected persons, but the molecular mechanisms underlying its pathogenesis and transmission remain unknown. Here, we propose a statistical physics model to coalesce all molecular entities into a cohesive network in which the roadmap of how each entity mediates the disease can be characterized. We argue that the process of how a transmitter transforms the virus into a recipient constitutes a triad unit that propagates COVID-19 along reticulate paths. Intrinsically, person-to-person transmissibility may be mediated by how genes interact transversely across transmitter, recipient, and viral genomes. We integrate quantitative genetic theory into hypergraph theory to code the main effects of the three genomes as nodes, pairwise cross-genome epistasis as edges, and high-order cross-genome epistasis as hyperedges in a series of mobile hypergraphs. Charting a genome-wide atlas of horizontally epistatic hypergraphs can facilitate the systematic characterization of the community genetic mechanisms underlying COVID-19 spread. This atlas can typically help design effective containment and mitigation strategies and screen and triage those more susceptible persons and those asymptomatic carriers who are incubation virus transmitters.

Role of neutrophil to lymphocyte ratio as a prognostic indicator for COVID-19

BACKGROUND

Coronavirus disease 2019 (COVID-19) is a new pandemic disease, associated with substantial morbidity and mortality. Its diagnosis requires centralized facilities and time.

AIMS

To describe the exposure history and clinical picture of the COVID-19 patients, to study the SARS-CoV-2 Virus load and some determinants that may correlate with its prognosis, and to evaluate the role of inflammatory index NLR as an early predictor of COVID-19 prognosis.

METHODOLOGY

A prospective follow-up study included laboratory-confirmed 179 COVID-19 cases out of 660 suspected COVID-19 cases, at El-Madinah El-Monawarah General Hospital in April 2020. Confirmed cases were managed by the Saudi Protocol and followed up every 2 weeks by PCR, neutrophil to lymphocyte ratio (NLR) for 1 month. Data were collected through a validated questionnaire and by qualified infection control staff.

RESULTS

The majority of the COVID-19 cases were 67 (37.4%) aged 30 to <45 years, 157 (87.7%) males, 76.0% working outside the medical field. 38.0% were asymptomatic and 26.3% had severe symptoms, while the main presenting symptoms were fever and dry cough (49.7% and 43.6%), respectively. The case fatality was 7.8%. The male, nonmedical occupation, and low level of education had a statistically significant relationship with the baseline PCR. There was an inverse significant correlation between baseline PCR readings and the recovery duration and health status outcomes. NLR was noted to be significantly higher among old age, illiterate nonmedical occupation, case with severe symptoms, MICU admission, and worst health status outcomes, but it was paradoxically higher among nonadmitted positive cases.

CONCLUSION

Admitted COVID-19 cases outcomes (disease severity, ICU admission, and mortality) significantly correlated to NLR and not to the baseline PCR viral load. NLR could be a beneficial prognostic and triaging parameter especially old nonmedical COVID-19 patients.

COVID-19 Pandemic: Public Health Risk Assessment and Risk Mitigation Strategies

A newly emerged respiratory viral disease called severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is also known as pandemic coronavirus disease (COVID-19). This pandemic has resulted an unprecedented global health crisis and devastating impact on several sectors of human lives and economies. Fortunately, the average case fatality ratio for SARS-CoV-2 is below 2%, much lower than that estimated for MERS (34%) and SARS (11%). However, COVID-19 has a much higher transmissibility rate, as evident from the constant increase in the count of infections worldwide. This article explores the reasons behind how COVID-19 was able to cause a global pandemic crisis. The current outbreak scenario and causes of rapid global spread are examined using recent developments in the literature, epidemiological features relevant to public health awareness, and critical perspective of risk assessment and mitigation strategies. Effective pandemic risk mitigation measures have been established and amended against COVID-19 diseases, but there is still much scope for upgrading execution and coordination among authorities in terms of organizational leadership's commitment and diverse range of safety measures, including administrative control measures, engineering control measures, and personal protective equipment (PPE). The significance of containment interventions against the COVID-19 pandemic is now well established; however, there is a need for its effective execution across the globe, and for the improvement of the performance of risk mitigation practices and suppression of future pandemic crises.

Merits and Demerits of Selective Isolation of Superspreaders: A Mathematical Modeling Study Based upon West Bengal (India) SARS-COV 2 Data

Context:

All COVID cases and their contacts are considered highly infectious requiring isolation, which blocks the COVID isolation beds and disrupts life in the community.

Aim:

To find out the effect of selective isolation and contact tracing of superspreaders as compared with the conventional ongoing protocol.

Settings and Design:

A mathematical model was designed to look at the effect of isolation and contact tracing of only those with high viral loads (superspreaders) on COVID-19 bed occupancy and overall mortality, in comparison with conventional protocol of isolation and contact tracing of all cases.

Materials and Methods:

An agent-based model, calibrated to the ongoing West Bengal COVID-19 data, was run for a total of 178 days to find out the effect of the interventions on COVID-19 bed occupancy and mortality.

Results:

There is an impressive reduction in the occupancy of COVID isolation beds, even with the preintervention testing rate with no negative impact on mortality.

Conclusions:

Strict isolation of superspreaders only, maybe highly effective in reducing the burden on health care and solving the COVID isolation bed crises if the testing rate is significantly increased.

Importance of untested infectious individuals for interventions to suppress COVID-19

The impact of the extent of testing infectious individuals on suppression of COVID-19 is illustrated from the early stages of outbreaks in Germany, the Hubei province of China, Italy, Spain and the UK. The predicted percentage of untested infected individuals depends on the specific outbreak but we found that they typically represent 60-80% of all infected individuals during the early stages of the outbreaks. We propose that reducing the underlying transmission from untested cases is crucial to suppress the virus. This can be achieved through enhanced testing in combination with social distancing and other interventions that reduce transmission such as wearing face masks. Once transmission from silent carriers is kept under control by these means, the virus could have been fully suppressed through fast isolation and contact tracing of tested cases.

Viral genome-based Zika virus transmission dynamics in a paediatric cohort during the 2016 Nicaragua epidemic

BACKGROUND

Nicaragua experienced a large Zika epidemic in 2016, with up to 50% of the population in Managua infected. With the domesticated Aedes aegypti mosquito as its vector, it is widely assumed that Zika virus transmission occurs within the household and/or via human mobility. We investigated these assumptions by using viral genomes to trace Zika transmission spatially.

METHODS

We analysed serum samples from 119 paediatric Zika cases participating in the long-standing Paediatric Dengue Cohort Study in Managua, which was expanded to include Zika in 2015. An optimal spanning directed tree was constructed by minimizing the differences in viral sequence diversity composition between patient nodes, where low-frequency variants were used to increase the resolution of the inferred Zika outbreak dynamics.

FINDINGS

Out of the 18 houses where pairwise difference in sample collection dates among all the household members was within 30 days, we only found two where viruses from individuals within the same household were up to 10th-most closely linked to each other genetically. We also identified a substantial number of transmission events involving long geographical distances (n=30), as well as potential super-spreading events in the estimated transmission tree.

INTERPRETATION

Our finding highlights that community transmission, often involving long geographical distances, played a much more important role in epidemic spread than within-household transmission.

FUNDING

This study was supported by an NUS startup grant (OMS) and grants R01 AI099631 (AB), P01 AI106695 (EH), P01 AI106695-03S1 (FB), and U19 AI118610 (EH) from the US National Institutes of Health.

Catch Me if You Can: Superspreading of COVID-19

While significant insights have been gained concerning COVID-19, superspreading of coronaviruses remains a mystery. The vast majority of cases have been linked to a relatively small portion of infected individuals. Yet, the genetic sequence of the virus, severity of disease, and underlying host parameters, such as age, sex, and health conditions, are not clearly driving the superspreading phenomenon. In this commentary we discuss what is known and what is not known about coronavirus superspreader transmission and explore whether characteristics of the virion, the donor, or the environment contribute to this phenomenon.

Age structuring and spatial heterogeneity in prion protein gene (PRNP) polymorphism in white-tailed deer

Chronic-wasting disease (CWD) is a prion-derived fatal neurodegenerative disease that has affected wild cervid populations on a global scale. Susceptibility has been linked unambiguously to several amino acid variants within the prion protein gene (PRNP). Quantifying their distribution across landscapes can provide critical information for agencies attempting to adaptively manage CWD. Here we attempt to further define management implications of PRNP polymorphism by quantifying the contemporary geographic distribution (i.e., phylogeography) of PRNP variants in hunter-harvested white-tailed deer (WTD; Odocoileus virginianus, N = 1433) distributed across Arkansas (USA), including a focal spot for CWD since detection of the disease in February 2016. Of these, PRNP variants associated with the well-characterized 96S non-synonymous substitution showed a significant increase in relative frequency among older CWD-positive cohorts. We interpreted this pattern as reflective of a longer life expectancy for 96S genotypes in a CWD-endemic region, suggesting either decreased probabilities of infection or reduced disease progression. Other variants showing statistical signatures of potential increased susceptibility, however, seemingly reflect an artefact of population structure. We also showed marked heterogeneity across the landscape in the prevalence of ‘reduced susceptibility’ genotypes. This may indicate, in turn, that differences in disease susceptibility among WTD in Arkansas are an innate, population-level characteristic that is detectable through phylogeographic analysis.

Superspreading in the emergence of COVID-19 variants

Superspreading and variants of concern (VOC) of the human pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are the main catalyzers of the coronavirus disease 2019 (COVID-19) pandemic. However, measuring their individual impact is challenging. By examining the largest database of SARS-CoV-2 genomes The Global Initiative on Sharing Avian Influenza Data [GISAID; n >1.2 million high-quality (HQ) sequences], we present evidence suggesting that superspreading has had a key role in the epidemiological predominance of VOC. There are clear signatures in the database compatible with large superspreading events (SSEs) coinciding chronologically with the worst epidemiological scenarios triggered by VOC. The data suggest that, without the randomness effect of the genetic drift facilitated by superspreading, new VOC of SARS-CoV-2 would have had more limited chance of success.

Estimation of the doubling time and reproduction number for COVID-19

In this paper, we have calculated the basic reproduction number (R₀) and doubling time (T_d) for the novel Coronavirus disease 2019 (COVID-19). The calculation is performed for March 2020 from the data provided by worldometer. We have investigated the data for Germany and Bangladesh. The calculation of R₀ is performed based on SIR model. The parameter T_d is estimated based on the new cases of each day. Since T_d and R₀ in use to judge the lockdowns and other measures to prevent spreading of the virus, we have provided simple approximation of both parameters.