Zoonotic spillover and viral mutations from low and middle-income countries: improving prevention strategies and bridging policy gaps.

The increasing frequency of zoonotic spillover events and viral mutations in low and middle-income countries presents a critical global health challenge. Contributing factors encompass cultural practices like bushmeat consumption, wildlife trade for traditional medicine, habitat disruption, and the encroachment of impoverished settlements onto natural habitats. The existing "vaccine gap" in many developing countries exacerbates the situation by allowing unchecked viral replication and the emergence of novel mutant viruses. Despite global health policies addressing the root causes of zoonotic disease emergence, there is a significant absence of concrete prevention-oriented initiatives, posing a potential risk to vulnerable populations. This article is targeted at policymakers, public health professionals, researchers, and global health stakeholders, particularly those engaged in zoonotic disease prevention and control in low and middle-income countries. The article underscores the importance of assessing potential zoonotic diseases at the animal-human interface and comprehending historical factors contributing to spillover events. To bridge policy gaps, comprehensive strategies are proposed that include education, collaborations, specialized task forces, environmental sampling, and the establishment of integrated diagnostic laboratories. These strategies advocate simplicity and unity, breaking down barriers, and placing humanity at the forefront of addressing global health challenges. Such a strategic and mental shift is crucial for constructing a more resilient and equitable world in the face of emerging zoonotic threats.

Sampling efficiency and screening of Aedes albopictus for yellow fever virus in Niger Delta region of Nigeria.

Introduction: Aedes albopictus, like Aedes aegypti, is a virulent vector of arboviruses especially the well-documented spread of yellow fever around the world. Although yellow fever is prevalent in Nigeria, there is a paucity of information in the Niger Delta region on the distribution of Aedes mosquito vectors and molecular detection of the virus in infected mosquitoes. This study sampled Aedes mosquitoes around houses associated with farms from four communities (Otolokpo, Ute-Okpu, Umunede, and Ute Alohen) in Ika North-East Local Government Area of Delta State, Nigeria. Methods: various sampling methods were used in Aedes mosquito collection to test their efficacy in the survey. Mosquitoes in holding cages were killed by freezing and morphologically identified. A pool of 15 mosquitoes per Eppendorf tube was preserved in RNAi later for yellow fever virus screening. Two samples were molecularly screened for each location. Results: seven hundred and twenty-five (725) mosquitoes were obtained from the various traps. The mean abundance of the mosquitoes was highest in m-HLC (42.9) compared to the mosquitoes sampled using other techniques (p<0.0001). The mean abundance of mosquitoes was lowest in Center for Disease Control (CDC) light traps without attractant (0.29). No yellow fever virus strain was detected in all the mosquitoes sampled at the four locations. Conclusion: this study suggests that Aedes albopictus are the mosquitoes commonly biting around houses associated with farms. More so, yellow fever virus was not detected in the mosquitoes probably due to the mass vaccination exercise that was carried out the previous year in the study area. More studies are required using the m-HLC to determine the infection rate in this endemic area.

Prevalence, under-reporting, and epidemiological surveillance of COVID-19 in the Araguaína City of Brazil.

Asymptomatic and underreported individuals remain a source of coronafig disease 2019 (COVID-19) transmission to others. Data on the prevalence and epidemiological factors influencing transmission are fundamental for establishing control measures, especially in vulnerable regions such as the Amazon. This study aimed to determine the point prevalence and active infection of COVID-19 among the population in Araguaína, a Brazilian city located in the Amazon region, analyzed the socioeconomic and behavioral variables of a statistically representative sample of this population using an epidemiological survey, and identify the viral genomic diversity in the region. During the sixth epidemiological week of 2021 (February 8 to 12), samples of 497 inhabitants of the municipality asymptomatic for respiratory syndromes underwent reverse transcription-quantitative polymerase chain reaction and serological tests (immunoglobulin M and immunoglobulin G). A questionnaire collated data on socioeconomic factors, prevention measures, and health status history. The active infection rate was 6.2%, and the prevalence was 13.5% of the study population. Active infection cases were under-reported; each reported positive case represented 14-28 under-reported cases. Lineages P.2, P.1, and B.1.1 were detected. Working from home was a protective factor against the infection, and clinical signs of fever, dry cough, and loss of taste or smell were associated with testing positive (p <0.05). A descriptive analysis of the indicators revealed that the entire population was susceptible to the disease. Intensified vaccination strategies are required regardless of socioeconomic factors, health conditions, and preventive measures. Implementation of objective, comprehensive, and efficient management tools to minimize the spread of COVID-19 in this municipality can serve as a model for other regions of Brazil.

High SARS-CoV-2 Exposure in Rural Southern Mozambique After Four Waves of COVID-19: Community-Based Seroepidemiological Surveys.

BACKGROUND: Mozambique was one of many African countries with limited testing capacity for SARS-CoV-2. Serosurveys, an alternative to estimate the real exposure to understand the epidemiology and transmission dynamics, have been scarce in Mozambique. Herein, we aimed to estimate the age-specific seroprevalence of SARS-CoV-2 in the general population of the Manhiça District, at four time points, for evaluating dynamics of exposure and the impact of vaccination. METHODS: We conducted four community-based seroepidemiological surveys separated by 3 months between May 2021 and June 2022 to assess the prevalence of SARS-CoV-2 antibodies. An age-stratified (0-19, 20-39, 40-59, and ≥ 60 years) sample of 4810 individuals was randomly selected from demographic surveillance database, and their blood samples were analyzed using WANTAI SARS-CoV-2 IgG + IgM ELISA. Nasopharyngeal swabs from a subsample of 2209 participants were also assessed for active infection by RT-qPCR. RESULTS: SARS-CoV-2 seroprevalence increased from 27.6% in the first survey (May 2021) to 63.6%, 91.2%, and 91.1% in the second (October 2021), third (January 2022), and fourth (May 2022) surveys, respectively. Seroprevalence in individuals < 18 years, who were not eligible for vaccination, increased from 23.1% in the first survey to 87.1% in the fourth. The prevalence of active infection was below 10.1% in all surveys. CONCLUSIONS: A high seroprevalence to SARS-CoV-2 was observed in the study population, including individuals not eligible for vaccination at that time, particularly after circulation of the highly transmissible Delta variant. These data are important to inform decision making on the vaccination strategies in the context of pandemic slowdown in Mozambique.

Avian flu: «for whom the bell tolls¼?

The family Orthomyxoviridae consists of 9 genera, including Alphainfluenza virus, which contains avian influenza viruses. In two subtypes H5 and H7 besides common low-virulent strains, a specific type of highly virulent avian virus have been described to cause more than 60% mortality among domestic birds. These variants of influenza virus are usually referred to as «avian influenza virus¼. The difference between high (HPAI) and low (LPAI) virulent influenza viruses is due to the structure of the arginine-containing proteolytic activation site in the hemagglutinin (HA) protein. The highly virulent avian influenza virus H5 was identified more than 100 years ago and during this time they cause outbreaks among wild and domestic birds on all continents and only a few local episodes of the disease in humans have been identified in XXI century. Currently, a sharp increase in the incidence of highly virulent virus of the H5N1 subtype (clade h2.3.4.4b) has been registered in birds on all continents, accompanied by the transmission of the virus to various species of mammals. The recorded global mortality rate among wild, domestic and agricultural birds from H5 subtype is approaching to the level of 1 billion cases. A dangerous epidemic factor is becoming more frequent outbreaks of avian influenza with high mortality among mammals, in particular seals and marine lions in North and South America, minks and fur-bearing animals in Spain and Finland, domestic and street cats in Poland. H5N1 avian influenza clade h2.3.4.4b strains isolated from mammals have genetic signatures of partial adaptation to the human body in the PB2, NP, HA, NA genes, which play a major role in regulating the aerosol transmission and the host range of the virus. The current situation poses a real threat of pre-adaptation of the virus in mammals as intermediate hosts, followed by the transition of the pre-adapted virus into the human population with catastrophic consequences.

Little-known virus is on the rise in South America.

Deforestation and climate change may be helping Oropouche virus spread far beyond the Amazon Basin.

Zoonotic human liver flukes, a type 1 biocarcinogen, in freshwater fishes: genetic analysis and confirmation of molluscan vectors and reservoir hosts in Bangladesh.

BACKGROUND: Opisthorchiid flukes, particularly Opisthorchis viverrini, Opisthorchis felineus, Clonorchis sinensis, and Metorchis spp. are the most common fish-borne zoonotic human liver flukes (hLFs). Liver fluke infections are more prevalent in resource-deprived and underprivileged areas. We herein estimated the prevalence of the metacercariae (MC) of major hLFs in common large freshwater fishes (lFWF) marketed for human consumption from some selected areas of Bangladesh along with detection of their molluscan vectors and reservoirs. METHODS: The current status of fish-borne zoonotic hLF infections in lFWF was investigated along with their molluscan vectors and mammalian reservoir hosts in Mymensingh and Kishoreganj in Bangladesh from July 2018-June 2022 using conventional and multiple molecular techniques, such as PCR, PCR-restriction fragment length polymorphism (RFLP), sequencing, and bioinformatic analyses. The infection rate of fishes was analyzed using the Z-test and the loads of MC were compared using the chi-squared (χ2) test. RESULTS: The MC of C. sinensis, Opisthorchis spp., and Metorchis spp. were detected in 11 species of common and popular lFWF. In lFWF, the estimated prevalence was 18.7% and the mean load was 137.4 ± 149.8 MC per 100 g of fish. The prevalence was the highest (P < 0.05) in spotted snakehead fishes (Channa punctata, 63.6%). The highest rate of infection (P < 0.05) was observed with the MC of C. sinensis (11.8%). Metacercariae were almost equally (P > 0.05) distributed between the head and body of fishes. The infection rate was slightly higher in cultured (19.6%) fishes. The MC of C. sinensis, O. felineus, O. viverrini, and Metorchis orientalis in fishes were confirmed using PCR, PCR-RFLP and bioinformatics. The cercariae of opisthorchiid (Pleurolophocercus cercariae) flukes were only recovered from Bithynia spp. (3.9%, 42 out of 1089). The ova of hLFs from dogs (4.3%, 5 out of 116) and cats (6.0%, 6 out of 100), and adult flukes (M. orientalis) from ducks (41.1% 113 out of 275) were detected. CONCLUSIONS: The MC of hLFs are highly prevalent in fresh water fishes in Bangladesh. Reservoir hosts, such as street dogs, cats, and ducks carried the patent infection, and residents of Bangladesh are at risk.

Optimal control strategies for toxoplasmosis disease transmission dynamics via harmonic mean-type incident rate.

Toxoplasma infection in humans is considered due to direct contact with infected cats. Toxoplasma infection (an endemic disease) has the potential to affect various organs and systems (brain, eyes, heart, lungs, liver, and lymph nodes). Bilinear incidence rate and constant population (birth rate is equal to death rate) are used in the literature to explain the dynamics of Toxoplasmosis disease transmission in humans and cats. The goal of this study is to consider the mathematical model of Toxoplasma disease with harmonic mean type incident rate and also consider that the population of humans and cats is not equal (birth rate and the death rate are not equal). In examining Toxoplasma transmission dynamics in humans and cats, harmonic mean incidence rates are better than bilinear incidence rates. The disease dynamics are first schematically illustrated, and then the law of mass action is applied to obtain nonlinear ordinary differential equations (ODEs). Analysis of the boundedness, positivity, and equilibrium points of the system has been analyzed. The reproduction number is calculated using the next-generation matrix technique. The stability of disease-free and endemic equilibrium are analyzed. Sensitivity analysis is also done for reproduction number. Numerical simulation shows that the infection is spread in the population when the contact rate ß h and ß c increases while the infection is reduced when the recovery rate δ h increases. This study investigates the impact of various optimal control strategies, such as vaccinations for the control of disease and the awareness of disease awareness, on the management of disease.

Intense malarial transmission during the dry season in irrigated rice-growing areas: a case study in Sakassou, Côte d'Ivoire.

The health district of Sakassou is one of the 83 health districts in Côte d'Ivoire, located in a zone with very high malarial transmission rates, with an incidence rate of ≥40% Therefore, to guide vector control methods more effectively, it was crucial to have a good understanding of the vectors in the area. This study aimed to determine the level of malarial transmission during the dry season in Sakassou, Côte d'Ivoire. Female Anopheles mosquitoes were sampled using human landing catches (HLCs) and pyrethrum spraying catches (PSCs). The larvae were collected using the 'dipping' method. A total of 10,875 adult female mosquitoes of Anopheles gambiae were collected. The PCR analysis revealed that all individuals were Anopheles coluzzii. The geographical distribution of potential breeding sites of Anopheles showed the presence of An. coluzzii in all the wetlands of the city of Sakassou. During the dry season, the human-biting rate of An. coluzzii was 139.1 bites/person/night. An exophagic trend was displayed by an adult female of An. coluzzii. The entomological inoculation rate during the dry season was 1.49 infectious bites/person/night. This study demonstrated that An. coluzzii was the main vector of malarial transmission in Sakassou, and the intensity of transmission remains high throughout the dry season.

Geospatial joint modeling of vector and parasite serology to microstratify malaria transmission.

The World Health Organization identifies a strong surveillance system for malaria and its mosquito vector as an essential pillar of the malaria elimination agenda. Anopheles salivary antibodies are emerging biomarkers of exposure to mosquito bites that potentially overcome sensitivity and logistical constraints of traditional entomological surveys. Using samples collected by a village health volunteer network in 104 villages in Southeast Myanmar during routine surveillance, the present study employs a Bayesian geostatistical modeling framework, incorporating climatic and environmental variables together with Anopheles salivary antigen serology, to generate spatially continuous predictive maps of Anopheles biting exposure. Our maps quantify fine-scale spatial and temporal heterogeneity in Anopheles salivary antibody seroprevalence (ranging from 9 to 99%) that serves as a proxy of exposure to Anopheles bites and advances current static maps of only Anopheles occurrence. We also developed an innovative framework to perform surveillance of malaria transmission. By incorporating antibodies against the vector and the transmissible form of malaria (sporozoite) in a joint Bayesian geostatistical model, we predict several foci of ongoing transmission. In our study, we demonstrate that antibodies specific for Anopheles salivary and sporozoite antigens are a logistically feasible metric with which to quantify and characterize heterogeneity in exposure to vector bites and malaria transmission. These approaches could readily be scaled up into existing village health volunteer surveillance networks to identify foci of residual malaria transmission, which could be targeted with supplementary interventions to accelerate progress toward elimination.

A behaviour and disease transmission model: incorporating the Health Belief Model for human behaviour into a simple transmission model.

The health and economic impacts of infectious diseases such as COVID-19 affect all levels of a community from the individual to the governing bodies. However, the spread of an infectious disease is intricately linked to the behaviour of the people within a community since crowd behaviour affects individual human behaviour, while human behaviour affects infection spread, and infection spread affects human behaviour. Capturing these feedback loops of behaviour and infection is a well-known challenge in infectious disease modelling. Here, we investigate the interface of behavioural science theory and infectious disease modelling to explore behaviour and disease (BaD) transmission models. Specifically, we incorporate a visible protective behaviour into the susceptible-infectious-recovered-susceptible (SIRS) transmission model using the socio-psychological Health Belief Model to motivate behavioural uptake and abandonment. We characterize the mathematical thresholds for BaD emergence in the BaD SIRS model and the feasible steady states. We also explore, under different infectious disease scenarios, the effects of a fully protective behaviour on long-term disease prevalence in a community, and describe how BaD modelling can investigate non-pharmaceutical interventions that target-specific components of the Health Belief Model. This transdisciplinary BaD modelling approach may reduce the health and economic impacts of future epidemics.

Mosquitocidal effect of ivermectin-treated nettings and sprayed walls on Anopheles gambiae s.s.

Ivermectin (IVM) has been proposed as a new tool for malaria control as it is toxic on vectors feeding on treated humans or cattle. Nevertheless, IVM may have a direct mosquitocidal effect when applied on bed nets or sprayed walls. The potential for IVM application as a new insecticide for long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS) was tested in this proof-of-concept study in a laboratory and semi-field environment. Laboratory-reared, insecticide-susceptible Kisumu Anopheles gambiae were exposed to IVM on impregnated netting materials and sprayed plastered- and mud walls using cone bioassays. The results showed a direct mosquitocidal effect of IVM on this mosquito strain as all mosquitoes died by 24 h after exposure to IVM. The effect was slower on the IVM-sprayed walls compared to the treated nettings. Further work to evaluate possibility of IVM as a new insecticide formulation in LLINs and IRS will be required.

Comprehensive knowledge of mother-to-child HIV/AIDS transmission, prevention, and associated factors among reproductive-age women in East Africa: insights from recent demographic and national health surveys.

BACKGROUND: More than 90% of babies acquire HIV/AIDS through vertical transmission, primarily due to low maternal comprehensive knowledge about Mother-To-Child Transmission (MTCT) of HIV/AIDS and its prevention, which is a cornerstone for eliminating MTCT of HIV/AIDS. However, there are limitations in terms of population data and literature evidence based on recent Demographic and Health Surveys (DHS) reports in East Africa. Therefore, this study aims to assess the comprehensive knowledge and PMTCT of HIV/AIDS among women, as well as the associated factors in East Africa. METHODS: Our data was obtained from the most recent DHS conducted in East African countries between 2011 and 2022. For our research, we included DHS data from ten nations, resulting in a total weighted sample of 133,724 women for our investigation. A generalized linear model (GLM) with a log link and binomial family to directly estimate prevalence ratios (PR) and 95% confidence intervals (CI) for the association between the independent variables, and the outcome variable. Finally, we reported the adjusted prevalence ratios along with their corresponding 95% CIs. Factors with p-values ≤ 0.2 for univariate logistic regression and < 0.05 were considered statistically significant factors of HIV/AIDS knowledge and prevention in the final model. RESULTS: In this study, 59.41% (95% CI: 59.15-59.67) of respondents had a comprehensive knowledge about MTCT of HIV/AIDS and its prevention among reproductive-age women in East Africa. Being in the older age group, better education level, being from a rich household, employment status, having ANC follow up, institutional delivery, and modern contraception usage were associated with higher prevalence ratios of comprehensive knowledge about MTCT of HIV/AIDS and its prevention. However, being single in marital status, rural women, and traditional contraception utilization were associated with lower ratios of comprehensive knowledge about MTCT of HIV/AIDS and its prevention. CONCLUSION: Our findings indicate a significant deficiency in comprehensive knowledge and prevention of HIV/AIDS MTCT among women in East Africa. These results emphasize the need for significant improvements in maternal-related health services. It is crucial to effectively target high-risk populations during interventions, raise awareness about this critical public health issue, and address the catastrophic consequences associated with MTCT. By implementing these measures, we can make substantial progress in reducing the transmission of HIV/AIDS from mother to child and ensuring better health outcomes for both mothers and their children.

The COVID-19 pandemic "anthropause" decreased plastic ingestion in neotropic cormorants Nannopterum brasilianus in Lima, Peru.

Background: The anthropause during the recent COVID-19 pandemic provided a unique opportunity to examine the impact of human activity on seabirds. Lockdowns in Peru prevented people from visiting coastal areas, thereby reducing garbage disposal on beaches and the movement of microplastics into the ocean. This cessation of activities likely led to a temporary decrease in plastic pollution in coastal regions. We aimed to investigate this phenomenon in inshore-feeding neotropic cormorants (Nannopterum brasilianus) along the Circuito de Playas Costa Verde (CPCV), situated on the coastal strip of Lima, Peru (∼ 11 million people). Methods: We collected and analyzed fresh pellets along the CPCV before (over 11 months) and during the pandemic lockdowns (over 8 months). Results: Our findings revealed a significant reduction in the occurrence of plastic in pellets during the pandemic period (% Oc = 2.47, n = 647 pellets) compared to pre-pandemic conditions (% Oc = 7.13, n = 800 pellets). The most common plastic debris item found in the pellets was threadlike microplastic. Additionally, our study highlights the direct correlation between human presence on beaches and the quantity of microplastics (mainly threadlike) found in cormorant pellets. We suggest that the reintroduction of these materials into the sea, previously accumulated on the coast, is likely facilitated by the movement and activity of beachgoers toward the ocean.

Bayesian inference for the onset time and epidemiological characteristics of emerging infectious diseases.

Background: Emerging infectious diseases pose a significant threat to global public health. Timely detection and response are crucial in mitigating the spread of such epidemics. Inferring the onset time and epidemiological characteristics is vital for accelerating early interventions, but accurately predicting these parameters in the early stages remains challenging. Methods: We introduce a Bayesian inference method to fit epidemic models to time series data based on state-space modeling, employing a stochastic Susceptible-Exposed-Infectious-Removed (SEIR) model for transmission dynamics analysis. Our approach uses the particle Markov chain Monte Carlo (PMCMC) method to estimate key epidemiological parameters, including the onset time, the transmission rate, and the recovery rate. The PMCMC algorithm integrates the advantageous aspects of both MCMC and particle filtering methodologies to yield a computationally feasible and effective means of approximating the likelihood function, especially when it is computationally intractable. Results: To validate the proposed method, we conduct case studies on COVID-19 outbreaks in Wuhan, Shanghai and Nanjing, China, respectively. Using early-stage case reports, the PMCMC algorithm accurately predicted the onset time, key epidemiological parameters, and the basic reproduction number. These findings are consistent with empirical studies and the literature. Conclusion: This study presents a robust Bayesian inference method for the timely investigation of emerging infectious diseases. By accurately estimating the onset time and essential epidemiological parameters, our approach is versatile and efficient, extending its utility beyond COVID-19.

The effectiveness of intervention measures on MERS-CoV transmission by using the contact networks reconstructed from link prediction data.

Introduction: Mitigating the spread of infectious diseases is of paramount concern for societal safety, necessitating the development of effective intervention measures. Epidemic simulation is widely used to evaluate the efficacy of such measures, but realistic simulation environments are crucial for meaningful insights. Despite the common use of contact-tracing data to construct realistic networks, they have inherent limitations. This study explores reconstructing simulation networks using link prediction methods as an alternative approach. Methods: The primary objective of this study is to assess the effectiveness of intervention measures on the reconstructed network, focusing on the 2015 MERS-CoV outbreak in South Korea. Contact-tracing data were acquired, and simulation networks were reconstructed using the graph autoencoder (GAE)-based link prediction method. A scale-free (SF) network was employed for comparison purposes. Epidemic simulations were conducted to evaluate three intervention strategies: Mass Quarantine (MQ), Isolation, and Isolation combined with Acquaintance Quarantine (AQ + Isolation). Results: Simulation results showed that AQ + Isolation was the most effective intervention on the GAE network, resulting in consistent epidemic curves due to high clustering coefficients. Conversely, MQ and AQ + Isolation were highly effective on the SF network, attributed to its low clustering coefficient and intervention sensitivity. Isolation alone exhibited reduced effectiveness. These findings emphasize the significant impact of network structure on intervention outcomes and suggest a potential overestimation of effectiveness in SF networks. Additionally, they highlight the complementary use of link prediction methods. Discussion: This innovative methodology provides inspiration for enhancing simulation environments in future endeavors. It also offers valuable insights for informing public health decision-making processes, emphasizing the importance of realistic simulation environments and the potential of link prediction methods.

Public transit mobility as a leading indicator of COVID-19 transmission in 40 cities during the first wave of the pandemic.

Background: The rapid global emergence of the COVID-19 pandemic in early 2020 created urgent demand for leading indicators to track the spread of the virus and assess the consequences of public health measures designed to limit transmission. Public transit mobility, which has been shown to be responsive to previous societal disruptions such as disease outbreaks and terrorist attacks, emerged as an early candidate. Methods: We conducted a longitudinal ecological study of the association between public transit mobility reductions and COVID-19 transmission using publicly available data from a public transit app in 40 global cities from March 16 to April 12, 2020. Multilevel linear regression models were used to estimate the association between COVID-19 transmission and the value of the mobility index 2 weeks prior using two different outcome measures: weekly case ratio and effective reproduction number. Results: Over the course of March 2020, median public transit mobility, measured by the volume of trips planned in the app, dropped from 100% (first quartile (Q1)-third quartile (Q3) = 94-108%) of typical usage to 10% (Q1-Q3 = 6-15%). Mobility was strongly associated with COVID-19 transmission 2 weeks later: a 10% decline in mobility was associated with a 12.3% decrease in the weekly case ratio (exp(ß) = 0.877; 95% confidence interval (CI): [0.859-0.896]) and a decrease in the effective reproduction number (ß = -0.058; 95% CI: [-0.068 to -0.048]). The mobility-only models explained nearly 60% of variance in the data for both outcomes. The adjustment for epidemic timing attenuated the associations between mobility and subsequent COVID-19 transmission but only slightly increased the variance explained by the models. Discussion: Our analysis demonstrated the value of public transit mobility as a leading indicator of COVID-19 transmission during the first wave of the pandemic in 40 global cities, at a time when few such indicators were available. Factors such as persistently depressed demand for public transit since the onset of the pandemic limit the ongoing utility of a mobility index based on public transit usage. This study illustrates an innovative use of "big data" from industry to inform the response to a global pandemic, providing support for future collaborations aimed at important public health challenges.

Unraveling tuberculosis patient cluster transmission chains: integrating WGS-based network with clinical and epidemiological insights.

Background: Tuberculosis remains a global health threat, and the World Health Organization reports a limited reduction in disease incidence rates, including both new and relapse cases. Therefore, studies targeting tuberculosis transmission chains and recurrent episodes are crucial for developing the most effective control measures. Herein, multiple tuberculosis clusters were retrospectively investigated by integrating patients' epidemiological and clinical information with median-joining networks recreated based on whole genome sequencing (WGS) data of Mycobacterium tuberculosis isolates. Methods: Epidemiologically linked tuberculosis patient clusters were identified during the source case investigation for pediatric tuberculosis patients. Only M. tuberculosis isolate DNA samples with previously determined spoligotypes identical within clusters were subjected to WGS and further median-joining network recreation. Relevant clinical and epidemiological data were obtained from patient medical records. Results: We investigated 18 clusters comprising 100 active tuberculosis patients 29 of whom were children at the time of diagnosis; nine patients experienced recurrent episodes. M. tuberculosis isolates of studied clusters belonged to Lineages 2 (sub-lineage 2.2.1) and 4 (sub-lineages 4.3.3, 4.1.2.1, 4.8, and 4.2.1), while sub-lineage 4.3.3 (LAM) was the most abundant. Isolates of six clusters were drug-resistant. Within clusters, the maximum genetic distance between closely related isolates was only 5-11 single nucleotide variants (SNVs). Recreated median-joining networks, integrated with patients' diagnoses, specimen collection dates, sputum smear microscopy, and epidemiological investigation results indicated transmission directions within clusters and long periods of latent infection. It also facilitated the identification of potential infection sources for pediatric patients and recurrent active tuberculosis episodes refuting the reactivation possibility despite the small genetic distance of ≤5 SNVs between isolates. However, unidentified active tuberculosis cases within the cluster, the variable mycobacterial mutation rate in dormant and active states, and low M. tuberculosis genetic variability inferred precise transmission chain delineation. In some cases, heterozygous SNVs with an allelic frequency of 10-73% proved valuable in identifying direct transmission events. Conclusion: The complex approach of integrating tuberculosis cluster WGS-data-based median-joining networks with relevant epidemiological and clinical data proved valuable in delineating epidemiologically linked patient transmission chains and deciphering causes of recurrent tuberculosis episodes within clusters.