Reproduction Number Research Articles

DYNAMICS OF BANDITRY ACTIVITIES: A MATHEMATICAL MODELING APPROACH

Banditry represents a significant worldwide social issue; in light of this, a mathematical model for the dynamics of banditry activities was developed. The model is intended to control banditry activities in society. The model encompasses six population classes and incorporates preventive measures such as preventing susceptible individuals from being kidnapped and taking the captured bandits into jail or detention. Bandits-free and bandits persistence were established. Also, the threshold parameter (Reproduction Number (Rc)) used to measure the level of banditry activities was established, as (Rc<1), banditry activities die out from the dynamical system, and if Rc>1 the banditry persists Rc>1, in the system. Numerical results demonstrate that banditry is effectively curbed when Rc<1 and persists otherwise. Stability analyses reveal the local asymptotic stability of the bandit-free equilibrium, further supported by global stability analysis employing the Lyapunov theorem. Empirical findings suggest that incarcerating captured bandits significantly outperforms lethal measures or preventing susceptible individuals from being kidnapped.

Estimating the population-level effects of nonpharmaceutical interventions when transmission rates of COVID-19 vary by orders of magnitude from one contact to another

Statistical physicists have long studied systems where the variable of interest spans many orders of magnitude, the classic example is the relaxation times of glassy materials, which are often found to follow power laws. A power-law dependence has been found for the probability of transmission of COVID-19, as a function of length of time a susceptible person is in contact with an infected person. This is in data from the United Kingdom's COVID-19 app. The amount of virus in infected people spans many orders of magnitude. Inspired by this, I assume that the power-law behavior found in COVID-19 transmission is due to the effective transmission rate varying over orders of magnitude from one contact to another. I then use a model from statistical physics to estimate that if a population all wear FFP2/N95 masks, this reduces the effective reproduction number for COVID-19 transmission by a factor of approximately nine. Published by the American Physical Society 2024

A systematic review of using population-level human mobility data to understand SARS-CoV-2 transmission

The emergence of SARS-CoV-2 into a highly susceptible global population was primarily driven by human mobility-induced introduction events. Especially in the early stages, understanding mobility was vital to mitigating the pandemic prior to widespread vaccine availability. We conducted a systematic review of studies published from January 1, 2020, to May 9, 2021, that used population-level human mobility data to understand SARS-CoV-2 transmission. Of the 5505 papers with abstracts screened, 232 were included in the analysis. These papers focused on a range of specific questions but were dominated by analyses focusing on the USA and China. The majority included mobile phone data, followed by Google Community Mobility Reports, and few included any adjustments to account for potential biases in population sampling processes. There was no clear relationship between methods used to integrate mobility and SARS-CoV-2 data and goals of analysis. When considering papers focused only on the estimation of the effective reproductive number within the US, there was no clear relationship identified between this measure and changes in mobility patterns. Our findings underscore the need for standardized, systematic ways to identify the source of mobility data, select an appropriate approach to using it in analysis, and reporting.

Mathematical modeling of cholera dynamics in the presence of antimicrobial utilization strategy

Antimicrobial resistance poses a significant threat to public health, particularly in cholera treatment. The emergence of antibiotic resistance, coupled with the sharp decline in pharmaceutical companies developing new cholera antibiotics, is a cause for concern. We formulate a multidrug-resistant (MDR) cholera epidemic model that incorporates a stage-switching strategy between two antibiotics to reduce the magnitude of resistance. The model is analyzed mathematically, and sensitivity analysis of the reproduction number is performed using sub-reproduction numbers. Stability analysis of the cholera-sensitive-only and cholera-resistant-only equilibria is investigated using Centre Manifold Theory. The model is calibrated through Markov Chain Monte Carlo simulations in Stan, showing stability at equilibrium points, which is further verified through numerical simulations. The simulations demonstrate an inverse relationship between the number of MDR cholera cases and the number of individuals receiving second-line treatment for cholera. This study suggests that the correct use of antibiotics can effectively manage the emergence of antimicrobial resistance. From a public health policy perspective, these findings emphasize the importance of antibiotic stewardship programs and the need for policies that promote the responsible use of existing antibiotics while encouraging the development of new treatment options. Such measures could help mitigate the global burden of MDR cholera and prevent further escalation of resistance.

A novel nonlinear SAZIQHR epidemic transmission model: mathematical modeling, simulation, and optimal control

Abstract This study presents a seven-compartmental nonlinear epidemic model to explore the dynamics and control of the epidemic. The model extends the traditional susceptible-infected-recovered (SIR) framework by including additional compartments for aware, infodemic, hospitalized, and quarantined populations. It also incorporates three Holling type II saturated nonlinear incidence rates to better represent the disease transmission process. The ‘infodemic population’ refers to those spreading misinformation about the disease, its spread, control, and treatment. The study examines disease dynamics in the absence of a vaccine and identifies two key equilibria: the disease-free equilibrium (DFE) and the endemic equilibrium (EE). It is found that the DFE is locally asymptotically stable when the basic reproduction number ( R 0 ) is below one and becomes unstable when R 0 exceeds one. The model’s behavior at R 0 = 1 is analyzed using center manifold theory, revealing a forward bifurcation. Additionally, the existence of a positive endemic equilibrium is confirmed for R 0 > 1 , with no backward bifurcation observed when R 0 ≤ 1 . The EE is also shown to be locally asymptotically stable when R 0 is greater than one. Furthermore, the study formulates and mathematically analyzes an optimal control problem. Finally, numerical simulations are presented to support the analytical results.

Synergistic Impact of Active Case Detection and Early Hospitalization for Controlling the Spread of Yellow Fever Outbreak in Nigeria: An Epidemiological Modeling and Optimal Control Analysis

Capturing the factors influencing yellow fever (YF) outbreaks is essential for effective public health interventions, especially in regions like Nigeria, where the disease poses significant health risks. This study explores the synergistic effects of active case detection (ACD) and early hospitalization on controlling YF transmission dynamics. We develop a dynamic model that integrates vaccination, active case detection, and hospitalization to enhance our understanding of disease spread and inform prevention strategies. Our methodology encompasses mechanistic dynamic modeling, optimal control analysis, parameter estimation, model fitting, and sensitivity analyses to study YF transmission dynamics, ensuring the robustness of control measures. We employ advanced mathematical techniques, including next-generation matrix methods, to accurately compute the reproduction number and assess outbreak transmissibility. Rigorous qualitative analysis of the model reveals two equilibria: disease-free and endemic, demonstrating global asymptotic stability and its impact on overall YF transmission dynamics, significantly affecting control and prevention mechanisms. Furthermore, through sensitivity analysis, we identify crucial parameters of the model that require urgent attention for more effective YF control. Moreover, our results highlight the critical roles of ACD and early hospitalization in reducing YF transmission. These insights provide a foundation for informed decision making and resource allocation in epidemic control efforts, ultimately contributing to the enhancement of public health strategies aimed at mitigating the impact of YF outbreaks.

Bottom-Up Effects of Various Plant Growth Promoting Treatments on Fitness Parameters of Hippodamia variegata.

Plant growth-promoting activities using biological, chemical, and organic fertilizers are well-documented for pest insects, their impacts on predators are less commonly studied. This research investigates whether bell pepper plants treated with plant growth-promoting rhizobacteria (PGPRs), arbuscular mycorrhizal fungi (AMF), vermicompost (30%), and zinc sulfate either separately or in selected combinations affect the nutrient indices and population growth traits of the ladybug predator, Hippodamia variegata (Goeze), when fed on aphids, Myzus persicae (Sulzer). Bell pepper plants were individually treated with two PGPRs (Bacillus subtilis and Pseudomonas fluorescens), one AMF (Glomus intraradices), soil amended with 30% vermicompost (v/v), and foliar application of zinc sulfate under greenhouse conditions. Combined treatments of AMF × B. subtilis and AMF × P. fluorescens were also tested. Nutritional indices and population growth parameters of predator were reared on the treated plants infested with aphids. Results showed that the efficiency of conversion of ingested food (ECI) in predator larvae was highest on B. subtilis-treated aphids and lowest on vermicompost-treated aphids. The relative growth rate (RGR) of predator was the highest on zinc sulfate-treated aphids and lowest on vermicompost-treated aphids. Predators fed on vermicompost-treated aphids had the lowest net reproductive rate (R₀) and intrinsic rate of increase (r), while R₀ was highest for predators fed on B. subtilis-treated aphids and r was highest on P. fluorescens- and B. subtilis-treated aphids. These findings suggest that zinc sulfate and biological fertilizers involving PGPRs can enhance the ecological fitness of predators and could be effective in biocontrol-based integrated pest management of aphids.

Unraveling dengue dynamics with data calibration from Palu and Jakarta: Optimizing active surveillance and fogging interventions

Dengue fever is a complex infectious disease driven by multiple factors, including viral dynamics, mosquito behavior, environmental conditions, and human behaviours. The intricate nature of its transmission and outbreaks necessitates an interdisciplinary approach, integrating expertise from fields such as mathematics and public health. In this research, we examine the role of active case finding and mosquito population reduction through fogging in dengue control using a mathematical model approach. Active case finding aims to identify undetected dengue cases, both asymptomatic and symptomatic, which can help prevent further transmission and reduce the likelihood of severe symptoms by enabling earlier treatment. The model was developed using a system of nine-dimensional nonlinear ordinary differential equations. We conducted a mathematical analysis of the equilibria and their stability based on the basic reproduction number (R0). Our analysis shows that the disease-free equilibrium is locally asymptotically stable when R0<1. Furthermore, when R0=1, the model may exhibit backward bifurcation , depending on the death rate induced by dengue. The higher the dengue-induced death rate, the greater the likelihood of backward bifurcation at R0=1. We used dengue incidence data from two Indonesian provinces, Jakarta and Palu, to calibrate the model parameter values. Our global sensitivity analysis on the basic reproduction number indicates that active case findings are more crucial in Palu compared to Jakarta. Conversely, Jakarta is more sensitive to the infection parameter than Palu. Our numerical continuation simulation shows that implementing fogging to control the mosquito population should carefully consider the intensity, timing, and duration of the intervention to achieve a more optimal results.

A spatio-temporal infection epidemic model with fractional order, general incidence, and vaccination analysis

The paper investigates a spatio-temporal SEIR epidemic model on a global level with fractional order. It employs four partial differential equations incorporating fractional derivatives and account for diffusion to characterize infection dynamics. By applying fixed-point theorem results, the paper establishes the existence, uniqueness, and boundedness of the solution. Equilibrium points are determined based on vaccination values and the basic reproduction number R0. Global stability of each equilibrium is confirmed using the Lyapunov direct method. Through simulations with a predictor–corrector algorithm, key insights into epidemiological dynamics are provided, elucidating the impact of vaccination on reducing disease transmission and altering R0. Trajectories of various compartments closely align with theoretical equilibrium points, affirming the model’s predictive precision. Furthermore, simulations indicate the potential for attaining disease-free equilibria with heightened vaccination rates, underscoring the pivotal role of vaccination strategies in epidemic control and disease eradication. It was shown that the fractional derivative order has no effect on the equilibrium stability but rather only on the convergence speed towards the equilibrium points.

Lassa fever outbreaks, mathematical models, and disease parameters: a systematic review and meta-analysis

Understanding the epidemiological parameters and transmission dynamics of Lassa fever, a significant public health threat in west Africa caused by the rodent-borne Lassa virus, is crucial for informing evidence-based interventions and outbreak response strategies. Therefore, our study aimed to collate and enhance understanding of the key epidemiological parameters of Lassa fever. We conducted a systematic review, searching PubMed and Web of Science for peer-reviewed studies published from database inception up to June 13, 2024, to compile and analyse key epidemiological parameters, mathematical models, and outbreaks of Lassa fever. English-language, peer-reviewed, original research articles were included if they reported on Lassa fever outbreak sizes, transmission models, viral evolution, transmission, natural history, severity, seroprevalence, or risk factors. Non-peer-reviewed literature was excluded. Data were extracted by two independent individuals from published literature, focusing on seroprevalence, transmissibility, epidemiological delays, and disease severity. We performed a meta-analysis to calculate pooled estimates of case-fatality ratios (CFRs) and the delay from symptom onset to hospital admission. This study is registered with PROSPERO (identifier number CRD42023393345). The database search returned 5614 potentially relevant studies, and a further 16 studies were identified from backward citation chaining. After de-duplication and exclusion, 193 publications met our inclusion criteria and provided 440 relevant parameter estimates in total. Although Lassa virus is endemic in west Africa, the spatiotemporal coverage of general-population seroprevalence estimates (ranging from 2·6% [6/232] to 58·2% [103/177]) was poor, highlighting the spatial uncertainty in Lassa fever risk. Similarly, only four basic reproduction number estimates (ranging from 1·13 to 1·40) were available. We estimated a pooled total random effect CFR of 33·5% (95% CI 25·8-42·2, I2=95%) and found potential variation in severity by geographical regions typically associated with specific Lassa virus lineages. We estimated a pooled total random effect mean symptom-onset-to-hospital-admission delay of 8·19 days (95% CI 7·31-9·06, I2=93%), but other epidemiological delays were poorly characterised in the existing literature. Our findings highlight the absence of empirical Lassa fever parameter estimates despite its high burden in west Africa. Improved surveillance approaches to capture mild cases in humans and to further cover rodent populations are needed to better understand Lassa fever transmission dynamics. Addressing these gaps is essential for developing accurate mathematical models and informing evidence-based interventions to mitigate the effect of Lassa fever on public health in endemic regions. UK Medical Research Council, National Institute for Health and Care Research, Academy of Medical Sciences, Wellcome, UK Department for Business, Energy, and Industrial Strategy, British Heart Foundation, Diabetes UK, Schmidt Foundation, Community Jameel, Royal Society, and Imperial College London. For the French translation of the abstract see Supplementary Materials section.

A diffusion-advection epidemic model with mass action infection mechanism and birth–death effect

This paper is concerned with a reaction–diffusion-advection SIS (susceptible-infected-susceptible) epidemic model with mass action infection mechanism and linear birth–death effect. We derive a variational expression of the basic reproduction number R0 and establish its threshold role between disease extinction and persistence. More importantly, we investigate asymptotic profiles of endemic equilibrium with respect to large advection or small motility of susceptible/infected individuals. Compared with three other closely related modeling systems in previous works, it turns out that our model is not only mathematically more difficult to tackle, but also the theoretical findings reveal rather different phenomena concerning spreading and spatial distribution of infectious diseases. These results may bring some prospective applications in disease control strategies.

Aggregation unveiled: A sequential modelling approach to bark beetle outbreaks

Tree-killing bark beetle infestations are a cause of massive coniferous forest mortality impacting forest ecosystems and the ecosystem services they provide. Models predicting bark beetle outbreaks are crucial for forest management and conservation, necessitating studies of the effect of epidemiological traits on the probability and severity of outbreaks. Due to the aggregation behaviour of beetles and host tree defence, this epidemiological interaction is highly non-linear and outbreak behaviour remains poorly understood, motivating questions about when an outbreak can occur, what determines outbreak severity, and how aggregation behaviour modulates these quantities. Here, we apply the principle of distributed delays to create a novel and mathematically tractable model for beetle aggregation in an epidemiological framework. We derive the critical outbreak threshold for the beetle emergence rate, which is a quantity analogous to the basic reproductive ratio, R0, for epidemics. Beetle aggregation qualitatively impacts outbreak potential from depending on the emergence rate alone in the absence of aggregation to depending on both emergence rate and initial beetle density when aggregation is required. Finally, we use a stochastic model to confirm that our deterministic model predictions are robust in finite populations.

Wastewater-based effective reproduction number and prediction under the absence of shedding information.

Estimating effective reproduction number (Re) and predicting disease incidences are essential to formulate effective strategies for disease control. Although recent studies developed models for inferring Re from wastewater-based data, they require information on shedding dynamics. Here, we proposed a framework of Re estimation and prediction without shedding information. The framework consists of a space-state model for smoothing wastewater-based data and a renewal equation modified for wastewater-based data. The applicability of the framework was tested with simulated data and real-world data on Influenza A virus (IAV) and SARS-CoV-2 concentration in wastewater in 2022/2023 season in the USA. We confirmed the state-space model effectively fits various simulated epidemic curves and real-world data. In simulations, we found wastewater-based Re (Reww) closely aligns with instantaneous clinical Re when shedding dynamics are rapid. For more prolonged shedding, Reww approximates a smoothed Re over time. We also observed the necessary sampling frequency to trace dynamics of wastewater concentration and Reww accurately in the framework varies depending on the precision of detection methods, the epidemic status, the transmissibility of infectious diseases, and shedding dynamics. By applying our framework to real-world data, we found Reww for SARS-CoV-2 showed similar trend and values to clinically-based Re. Reww for IAV ranged from 0.66 to 1.52 with a clear peak in the winter season, which agrees with previously reported Re. We also succeeded in predicting wastewater concentration in a few weeks from available wastewater-based data. These results indicate that our framework potentially enables near real-time monitoring of approximated Re and prediction of infectious disease dynamics through wastewater surveillance, which limits the delay between infection and reporting. Our framework is useful especially for regions where reliable clinical surveillance is not available and notifiable surveillance is abolished, and can be expanded to multiple infectious diseases that have been detected from wastewater.

Optimal control for an SIR model with limited hospitalised patients

This paper analyses the optimal control of infectious disease propagation using a classic susceptible-infected-recovered (SIR) model characterised by permanent immunity and the absence of available vaccines. The control is performed over a time-dependent mean reproduction number, in order to minimise the cumulative number of ever-infected individuals (recovered), under different constraints. We consider constraints on non-pharmaceutical interventions ranging from partial lockdown to non-intervention, as well as the social and economic costs associated with such interventions, and the capacity limitations of intensive care units that limits the number of infected individuals to a maximum allowed value. We rigorously derive an optimal quarantine strategy based on necessary optimality conditions. The obtained optimal strategy is of a boundary-bang type, comprising three phases: an initial phase with no intervention, a second phase maintaining the infected population at its maximum possible value, and a final phase of partial lockdown applied over a single interval. The optimal policy is further refined by optimising the transition times between these phases. We show that these results are in excellent agreement with the numerical solution of the problem.

Density-dependent fitness and mass-rearing of Habrobracon hebetor (Say) (Hymenoptera: Braconidae) incorporating life table variability

The ectoparasitoid Habrobracon hebetor (Say) (Hymenoptera: Braconidae) is an important natural enemy insect deployed for controlling lepidopteran larvae in warehouses and fields. However, there is limited information on mass-rearing methods of H. hebetor. This study investigated the effects of different Ephestia elutella (Hübner) (Lepidoptera: Pyralidae) and parasitic egg densities on the fitness and mass-rearing efficiency of H. hebetor, aiming to determine the optimal conditions for developing an economically viable and sustainable mass-rearing system for this parasitoid. Our results showed that the average number of eggs laid per host dropped below 7.54 eggs when the number of hosts exceeded five. H. hebetor exhibited significantly increased preadult survival rate (67.41%-81.00%), female and male body size (2224.55-2379.83 μm, 2213.39-2324.65 μm), fecundity (443.98-492.43 eggs), net reproductive rate (R0=154.57-216.67 offspring), intrinsic rate of increase (r=0.2776-0.3223 d-1), net paralyzed rate (C0=72.37-93.17 larvae), and net parasitism rate (Z0=43.55-55.04 larvae) at a density of 5-15 eggs per host than at 20 eggs per host. Meanwhile, considering rearing costs and production rate, a density of 5-15 eggs per host proved to be the most economical for mass-rearing H. hebetor, achieving a daily harvest rate of 10,000 newly emerged female adults. Therefore, for an efficient mass-rearing system for H. hebetor, maintaining a density of 5-15 eggs per host is recommended considering variability in life table and discarding aged individuals (after 36 days old). In summary, providing 5-10 5th instar larvae of E. elutella per day per pair of male and female parasitoid wasps is advised for effective mass-rearing of H. hebetor.

Epidemiological Implications of Vertical Transmission and Nonlinear Treatment in Lassa fever: A Mathematical Study

Lassa fever is a viral hemorrhagic disease primarily transmitted through contact with food or household items contaminated by urine or feces of infected rodents. The disease poses a significant health risk in endemic regions, yet the effect of vertical transmission and non-linear treatment on its spread has not been thoroughly explored. To address this, a mathematical model was constructed to assess the effect of vertical transmission and non-linear treatment in the transmission dynamics of Lassa fever. The model was validated through the theory of positivity and boundedness, ensuring that its solution remains biologically meaningful over time. The existence of equilibrium points was examined and the basic reproduction number was calculated using the next generation matrix operator. Bifurcation analysis is performed using the Center Manifold Theory. The Local and global stability of the model around the Lassa fever free equilibrium is investigated using Jacobian Matrix method and the theorem proposed by Castillo-Chavez. The effect of the parameters of the basic reproduction number was investigated using normalized forward sensitivity index. Furthermore, it was inferred that decrease in the contact rate and the rate of vertical transmission were instrumental to curtailing the spread of Lassa fever in the population.

Effects of fine particulate matter air pollution on survival of Heliconius ethilla (Godart, 1819)

Human activities affect natural ecosystems worldwide and can generate negative effects on insect species such as growth inhibition, developmental abnormalities and reduction of reproductive and survival rates. Our study focused on fine particulate matter, a pollutant known to cause mechanical obstruction, heavy metal intoxication, and stress in Lepidoptera larvae. Heliconius ethilla (Lepidoptera: Nymphalidae) is a Heliconiinae found in the southeastern region of Brazil. We started our study with 3255 eggs, from which 69 stage 3 larvae were randomly separated as the treatment group. The larvae were fed with Passiflora edulis leaves and the SPM was added before being offered to the treatment group in an increasing concentration according to the age of the larvae and the leaf, simulating the gradual deposition of SPM in the environment. Our results demonstrate that Sedimentable Particulate Matter negatively impacted mortality rates, pupal weight, and body size. The results strongly indicate that the presence of SPM negatively impacts the survival, development and potentially the reproductive success of H. ethilla. A reduction in the population size and a consequent decrease in the chances of long-term survival of the species can be expected.

Dynamics of classical solutions of a multi-strain diffusive epidemic model with mass-action transmission mechanism.

We study a diffusive epidemic model and examine the spatial spreading dynamics of a multi-strain infectious disease. In particular, we address the questions of competitive-exclusion or coexistence of the disease's strains. Our results indicate that if one strain has its local reproduction function spatially homogeneous, which either strictly minimizes or maximizes the basic reproduction numbers, then the phenomenon of competitive-exclusion occurs. However, if all the local reproduction functions are spatially heterogeneous, several strains may coexist. In this case, we provide complete information on the large time behavior of classical solutions for the two-strain model when the diffusion rate is uniform within the population and the ratio of the local transmission rates is constant. Particularly, we prove the existence of two critical superimposed functions that serve as threshold values for the ratio of the transmission rates and that of the recovery rates. Furthermore, when the populations' diffusion rates are small, our result on the asymptotic profiles of coexistence endemic equilibria indicate a spatial segregation of infected populations.

Dynamic analysis of an HIV model with CTL immune response and logarithmic Ornstein–Uhlenbeck process

In this paper, we present a stochastic HIV model, including logistic growth, CTL immune response and logarithmic mean–reverting Ornstein–Uhlenbeck process. Firstly, we certify that there exists a unique global positive solution by rigorous mathematical analysis. Then, by constructing a suitable Lyapunov function and using the strong law of large numbers and Fatou’s lemma, we obtain the existence of the stationary distribution when stochastic reproduction number is greater than one. Moreover, we derive an approximate expression for the probability density function of the stochastic model around the infected equilibrium of its corresponding deterministic model. In addition, a critical condition for virus extinction is established. Finally, through numerical simulations, we investigate the effects of noise intensity and the logistic growth on model behavior, and we also explore the influence of key parameters on virus persistence and extinction.

Rapid spread of the SARS-CoV-2 Omicron XDR lineage derived from recombination between XBB and BA.2.86 subvariants circulating in Brazil in late 2023.

Recombination plays a crucial role in the evolution of SARS-CoV-2. The Omicron XBB* recombinant lineages are a noteworthy example, as they have been the dominant SARS-CoV-2 variant worldwide in the first half of 2023. Since November 2023, a new recombinant lineage between Omicron subvariants XBB and BA.2.86, designated XDR, has been detected mainly in Brazil. In this study, we reconstructed the spatiotemporal dynamics and estimated the absolute and relative transmissibility of the XDR lineage. The XDR lineage displayed a recombination breakpoint in the ORF1a-coding region, and the most closely related sequences to the 5' and 3' ends of the recombinant correspond to JD.1.1 and JN.1.1 lineages, respectively. The first XDR sequences were detected in November 2023 in the Northeastern Brazilian region, and their prevalence rapidly surged from <1% to 25% by February 2024. The Bayesian phylogeographic analysis supports that the XDR lineage likely emerged in the Northeastern Brazilian region around late October 2023 and rapidly disseminated within and outside Brazilian borders from mid-November onward. The median effective reproductive number of the XDR lineage in Brazil during the initial expansion phase was estimated to be around 1.5, and the average relative instantaneous reproduction numbers of XDR and JN* lineages were estimated to be 1.37 and 1.29 higher than that of co-circulating XBB* lineages. In summary, these findings support that the recombinant lineage XDR arose in the Northeastern Brazilian region in October 2023, shortly after the first detection of JN.1 sequences in the country. In Brazil, the XDR lineage exhibited a higher transmissibility level than its parental XBB.* lineages and is spreading at a rate similar to or slightly faster than the JN.1* lineages.IMPORTANCEThis study highlights the emergence and rapid dissemination of the recombinant SARS-CoV-2 XDR lineage, derived from the Omicron lineages JD.1.1 and JN.1.1. The XDR lineage exhibited equivalent transmissibility to its JN.1* parental lineages and quickly spread across Brazil in late 2023. The findings underscore the critical role of real-time genomic surveillance in detecting novel variants with higher transmission potential. By utilizing phylogenetic and epidemiological methods, this research provides important insights into the molecular dynamics of XDR, which could inform public health responses and vaccine composition updates. The study's significance lies in its ability to document the impact of recombination on viral evolution, offering valuable information to the field of virology and pandemic preparedness.