Analysis of a fractional-order model for dengue transmission dynamics with quarantine and vaccination measures.

A comprehensive mathematical model is proposed to study two strains of dengue virus with saturated incidence rates and quarantine measures. Imperfect dengue vaccination is also assumed in the model. Existence, uniqueness and stability of the proposed model are proved using the results from fixed point and degree theory. Additionally, well constructed Lyapunov function candidates are also applied to prove the global stability of infection-free equilibria. It is also demonstrated that the model is generalized Ulam-Hyers stable under some appropriate conditions. The model is fitted to the real data of dengue epidemic taken from the city of Espirito Santo in Brazil. For the approximate solution of the model, a non-standard finite difference(NSFD) approach is applied. Sensitivity analysis is also carried out to show the influence of different parameters involved in the model. The behaviour of the NSFD is also assessed under different denominator functions and it is observed that the choice of the denominator function could influence the solution trajectories. Different scenario analysis are also assessed when the reproduction number is below or above one. Furthermore, simulations are also presented to assess the epidemiological impact of dengue vaccination and quarantine measures for infected individuals.

Transmission dynamics of Zika virus with multiple infection routes and a case study in Brazil.

The Zika virus (ZIKV) is a serious global public health crisis. A major control challenge is its multiple transmission modes. This paper aims to simulate the transmission patterns of ZIKV using a dynamic process-based epidemiological model written in ordinary differential equations, which incorporates the human-to-mosquito infection by bites and sewage, mosquito-to-human infection by bites, and human-to-human infection by sex. Mathematical analyses are carried out to calculate the basic reproduction number and backward bifurcation, and prove the existence and stability of the equilibria. The model is validated with infection data by applying it to the 2015-2016 ZIKV epidemic in Brazil. The results indicate that the reproduction number is estimated to be 2.13, in which the contributions by mosquito bite, sex and sewage account for 85.7%, 3.5% and 10.8%, respectively. This number and the morbidity rate are most sensitive to parameters related to mosquito ecology, rather than asymptomatic or human-to-human transmission. Multiple transmission routes and suitable temperature exacerbate ZIKV infection in Brazil, and the vast majority of human infection cases were prevented by the intervention implemented. These findings may provide new insights to improve the risk assessment of ZIKV infection.

Modelling the unexpected dynamics of COVID-19 in Manaus, Brazil.

In late March 2020, SARS-CoV-2 arrived in Manaus, Brazil, and rapidly developed into a large-scale epidemic that collapsed the local health system and resulted in extreme death rates. Several key studies reported that ∼76% of residents of Manaus were infected (attack rate AR≃76%) by October 2020, suggesting protective herd immunity had been reached. Despite this, an unexpected second wave of COVID-19 struck again in November and proved to be larger than the first, creating a catastrophe for the unprepared population. It has been suggested that this could be possible if the second wave was driven by reinfections. However, it is widely reported that reinfections were at a low rate (before the emergence of Omicron), and reinfections tend to be mild. Here, we use novel methods to model the epidemic from mortality data without considering reinfection-caused deaths and evaluate the impact of interventions to explain why the second wave appeared. The method fits a "flexible" reproductive number R0(t) that changes over the epidemic, and it is demonstrated that the method can successfully reconstruct R0(t) from simulated data. For Manaus, the method finds AR≃34% by October 2020 for the first wave, which is far less than required for herd immunity yet in-line with seroprevalence estimates. The work is complemented by a two-strain model. Using genomic data, the model estimates transmissibility of the new P.1 virus lineage as 1.9 times higher than that of the non-P.1. Moreover, an age class model variant that considers the high mortality rates of older adults show very similar results. These models thus provide a reasonable explanation for the two-wave dynamics in Manaus without the need to rely on large reinfection rates, which until now have only been found in negligible to moderate numbers in recent surveillance efforts.

Epidemiological findings, estimates of the instantaneous reproduction number, and control strategies of the first Mpox outbreak in Latin America.

BACKGROUND: The 2022-2023 period marked the largest global Mpox outbreak, with Latin America's situation notably underexplored. This study aims to estimate Mpox's instantaneous reproduction number (R(t)), analyze epidemiological trends, and map vaccination efforts in six Latin American countries. METHODS: Utilizing Pan American Health Organization Mpox surveillance data, we examined demographic characteristics, cumulative incidence rates, and epidemic curves, calculated R(t) with weekly sliding windows for each country, alongside a review of vaccination initiatives. RESULTS: From 2022 to 2023, 25,503 Mpox cases and 71 deaths were reported across Argentina, Brazil, Chile, Colombia, Mexico and Peru, with a significant majority (91.8%-98.5%) affecting men, with a mean age of 32-35 years. Maximum R(t) values varied across countries: Argentina (2.63; 0.85 to 5.39), Brazil (3.13; 2.61 to 3.69), Chile (2.91; 1.55 to 4.70), Colombia (3.15; 2.07 to 4.44), Mexico (2.28; 1.18 to 3.75), and Peru (2.84; 2.33 to 3.40). The epidemic's peak occurred between August and September 2022 with R(t) values subsequently dropping below 1. From November 2022, and as of February 2024, only Chile, Peru, and Brazil had initiated Mpox vaccination campaigns, with Colombia launching a Clinical Trial. CONCLUSION: The peak of the Mpox epidemic in the studied countries occurred before the commencement of vaccination programs. This trend may be then partly attributed to a combination of behavioral modifications in key affected communities and contact tracing local programs. Therefore, the proportion of the at-risk population that remains susceptible is still uncertain, highlighting the need for continued surveillance and evaluation of vaccination strategies.

Spatial analysis of Dengue through the reproduction numbers relating to socioeconomic features: Case studies on two Brazilian urban centers.

The study of the propagation of infectious diseases in urban centers finds a close connection with their population's social characteristics and behavior. This work performs a spatial analysis of dengue cases in urban centers based on the basic reproduction numbers, R0, and incidence by planning areas (PAs), as well as their correlations with the Human Development Index (HDI) and the number of trips. We analyzed dengue epidemics in 2002 at two Brazilian urban centers, Belo Horizonte (BH) and Rio de Janeiro (RJ), using PAs as spatial units. Our results reveal heterogeneous spatial scenarios for both cities, with very weak correlations between R0 and both the number of trips and the HDI; in BH, the values of R0 show a less spatial heterogeneous pattern than in RJ. For BH, there are moderate correlations between incidence and both the number of trips and the HDI; meanwhile, they weakly correlate for RJ. Finally, the absence of strong correlations between the considered measures indicates that the transmission process should be treated considering the city as a whole.

Comparative Epidemiological Assessment of Monkeypox Infections on a Global and Continental Scale Using Logistic and Gompertz Mathematical Models.

The monkeypox virus (MPXV) has caused an unusual epidemiological scenario-an epidemic within a pandemic (COVID-19). Despite the inherent evolutionary and adaptive capacity of poxviruses, one of the potential triggers for the emergence of this epidemic was the change in the status of orthopoxvirus vaccination and eradication programs. This epidemic outbreak of HMPX spread worldwide, with a notable frequency in Europe, North America, and South America. Due to these particularities, the objective of the present study was to assess and compare cases of HMPX in these geographical regions through logistic and Gompertz mathematical modeling over one year since its inception. We estimated the highest contagion rates (people per day) of 690, 230, 278, and 206 for the world, Europe, North America, and South America, respectively, in the logistic model. The equivalent values for the Gompertz model were 696, 268, 308, and 202 for the highest contagion rates. The Kruskal-Wallis Test indicated different means among the geographical regions affected by HMPX regarding case velocity, and the Wilcoxon pairwise test indicated the absence of significant differences between the case velocity means between Europe and South America. The coefficient of determination (R2) values in the logistic model varied from 0.8720 to 0.9023, and in the Gompertz model, they ranged from 0.9881 to 0.9988, indicating a better fit to the actual data when using the Gompertz model. The estimated basic reproduction numbers (R0) were more consistent in the logistic model, varying from 1.71 to 1.94 in the graphical method and from 1.75 to 1.95 in the analytical method. The comparative assessment of these mathematical modeling approaches permitted the establishment of the Gompertz model as the better-fitting model for the data and the logistic model for the R0. However, both models successfully represented the actual HMPX case data. The present study estimated relevant epidemiological data to understand better the geographic similarities and differences in the dynamics of HMPX.

Trypanosoma cruzi infection in dogs along the US-Mexico border: R0 changes with vector species composition.

Infection with Trypanosoma cruzi, etiological agent of Chagas disease, is common in US government working dogs along the US-Mexico border. This 3145 km long border comprises four states: Texas (TX), New Mexico (NM), Arizona (AZ) and California (CA) with diverse ecosystems and several triatomine (a.k.a., kissing bug) species, primary vectors of T. cruzi in this region. The kissing bug (Heteroptera: Reduviidae) community ranging from CA to TX includes Triatoma protracta (Uhler), Triatoma recurva (Stål) and Triatoma rubida (Uhler) and becomes dominated by Triatoma gerstaeckeri Stål in TX. Here, we ask if T. cruzi infection dynamics in dogs varies along this border region, potentially reflecting changes in vector species and their vectorial capacity. Using reversible catalytic models of infection, where seropositivity can be lost, we estimated an R0 (Estimate ± S.E.) of 1.192 ± 0.084 for TX and NM. In contrast, seropositivity decayed to zero as dogs aged in AZ and CA. These results suggest that dogs are likely infected by T. cruzi during their training in western TX, with a force of infection large enough for keeping R0 above 1, i.e., the disease endemically established, in TX and NM. In AZ and CA, a lower force of infection, probably associated with different vector species communities and associated vectorial capacity and/or different lineages of T. cruzi, results in dogs decreasing their seropositivity with age.

A computational approach to identifiability analysis for a model of the propagation and control of COVID-19 in Chile.

A computational approach is adapted to analyze the parameter identifiability of a compartmental model. The model is intended to describe the progression of the COVID-19 pandemic in Chile during the initial phase in early 2020 when government declared quarantine measures. The computational approach to analyze the structural and practical identifiability is applied in two parts, one for synthetic data and another for some Chilean regional data. The first part defines the identifiable parameter sets when these recover the true parameters used to create the synthetic data. The second part compares the results derived from synthetic data, estimating the identifiable parameter sets from regional Chilean epidemic data. Experiments provide evidence of the loss of identifiability if some initial conditions are estimated, the period of time used to fit is before the peak, and if a significant proportion of the population is involved in quarantine periods.

An Epidemiological Analysis for Assessing and Evaluating COVID-19 Based on Data Analytics in Latin American Countries.

This research provides a detailed analysis of the COVID-19 spread across 14 Latin American countries. Using time-series analysis and epidemic models, we identify diverse outbreak patterns, which seem not to be influenced by geographical location or country size, suggesting the influence of other determining factors. Our study uncovers significant discrepancies between the number recorded COVID-19 cases and the real epidemiological situation, emphasizing the crucial need for accurate data handling and continuous surveillance in managing epidemics. The absence of a clear correlation between the country size and the confirmed cases, as well as with the fatalities, further underscores the multifaceted influences on COVID-19 impact beyond population size. Despite the decreased real-time reproduction number indicating quarantine effectiveness in most countries, we note a resurgence in infection rates upon resumption of daily activities. These insights spotlight the challenge of balancing public health measures with economic and social activities. Our core findings provide novel insights, applicable to guiding epidemic control strategies and informing decision-making processes in combatting the pandemic.

Modeling the SARS-CoV-2 Omicron variant dynamics in the United States with booster dose vaccination and waning immunity.

We carried out a theoretical and numerical analysis for an epidemic model to analyze the dynamics of the SARS-CoV-2 Omicron variant and the impact of vaccination campaigns in the United States. The model proposed here includes asymptomatic and hospitalized compartments, vaccination with booster doses, and the waning of natural and vaccine-acquired immunity. We also consider the influence of face mask usage and efficiency. We found that enhancing booster doses and using N95 face masks are associated with a reduction in the number of new infections, hospitalizations and deaths. We highly recommend the use of surgical face masks as well, if usage of N95 is not a possibility due to the price range. Our simulations show that there might be two upcoming Omicron waves (in mid-2022 and late 2022), caused by natural and acquired immunity waning with respect to time. The magnitude of these waves will be 53% and 25% lower than the peak in January 2022, respectively. Hence, we recommend continuing to use face masks to decrease the peak of the upcoming COVID-19 waves.

Arbovirus Transmission Predictions Are Affected by Both Temperature Data Source and Modeling Methodologies across Cities in Colombia.

Weather variables has been described as major drivers of vector proliferation and arbovirus transmission. Among them, temperature has consistently been found to be impactful in transmission dynamics, and models that incorporate temperature have been widely used to evaluate and forecast transmission or arboviruses like dengue, zika, or chikungunya virus. Further, there is growing evidence of the importance of micro-environmental temperatures in driving transmission of Aedes aegypti-borne viruses, as these mosquitoes tend to live within domiciles. Yet there is still a considerable gap in our understanding of how accounting for micro-environmental temperatures in models varies from the use of other widely-used, macro-level temperature measures. This effort combines field-collected data of both indoor and outdoor household associated temperatures and weather station temperature data from three Colombian cities to describe the relationship between the measures representing temperature at the micro- and macro-levels. These data indicate that weather station data may not accurately capture the temperature profiles of indoor micro-environments. However, using these data sources, the basic reproductive number for arboviruses was calculated by means of three modeling efforts to investigate whether temperature measure differences translated to differential transmission predictions. Across all three cities, it was determined that the modeling method was more often impactful rather than the temperature data-source, though no consistent pattern was immediately clear. This suggests that temperature data sources and modeling methods are important for precision in arbovirus transmission predictions, and more studies are needed to parse out this complex interaction.

Does Widespread Use of Hydroxychloroquine Reduce the Transmissibility of SARS-CoV-2 / COVID-19? An Ecological Correlational Study.

BACKGROUND: At the beginning of the coronavirus disease (COVID-19) pandemic, hydroxychloroquine (HCQ) was widely used as a possible antiviral agent. Current knowledge indicates that HCQ has little or no effect on individual clinical outcomes of COVID-19, but populational effects on disease transmissibility are still unknown. OBJECTIVE: This study investigates the hypothesis that massive HCQ consumption by a population may contribute to reducing the transmissibility of SARS-CoV-2 and COVID-19 spread by reducing the viral load of infected individuals. METHODS: Public database of seven states from Brazil in 2020 were assessed, before the start of COVID-19 vaccination. The daily values of the COVID-19 effective reproduction number (Rt) were obtained. Associations between Rt values and the proposed predictor variables (prevalence of COVID-19 as a marker of collective immunity; social isolation indices; consumption of HCQ) were tested using multiple linear regression analysis. RESULTS: In all seven states, consumption of HCQ was a significant negative predictor of Rt (ß ranged from -0.295 to -0.502, p = 0.001). Furthermore, the mean derivative of Rt during the declining period of the COVID-19 incidence (the mean rate of variation) was also significantly negatively related to the mean HCQ consumption in that period (R2 = 0.895; ß = -0.783; p = 0.011), meaning that the higher the HCQ consumption, the faster the decline of COVID-19 Rt. It suggests a dose-response phenomenon and a causal relationship in this association. CONCLUSION: The results of this study are compatible with the hypothesis that HCQ has small but significant in vivo antiviral effects that are able to reduce SARS-CoV-2 transmissibility at the populational level.

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

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

Modelling quarantine effects on SARS-CoV-2 epidemiological dynamics in Chilean communes and their relationship with the Social Priority Index.

Background: An epidemiological model (susceptible, un-quarantined infected, quarantined infected, confirmed infected (SUQC)) was previously developed and applied to incorporate quarantine measures and calculate COVID-19 contagion dynamics and pandemic control in some Chinese regions. Here, we generalized this model to incorporate the disease recovery rate and applied our model to records of the total number of confirmed cases of people infected with the SARS-CoV-2 virus in some Chilean communes. Methods: In each commune, two consecutive stages were considered: a stage without quarantine and an immediately subsequent quarantine stage imposed by the Ministry of Health. To adjust the model, typical epidemiological parameters were determined, such as the confirmation rate and the quarantine rate. The latter allowed us to calculate the reproduction number. Results: The mathematical model adequately reproduced the data, indicating a higher quarantine rate when quarantine was imposed by the health authority, with a corresponding decrease in the reproduction number of the virus down to values that prevent or decrease its exponential spread. In general, during this second stage, the communes with the lowest social priority indices had the highest quarantine rates, and therefore, the lowest effective viral reproduction numbers. This study provides useful evidence to address the health inequity of pandemics. The mathematical model applied here can be used in other regions or easily modified for other cases of infectious disease control by quarantine.

HIV/AIDS Mathematical Model of Triangle Transmission.

In this paper, a mathematical analysis of the HIV/AIDS deterministic model studied in the paper called Mathematical Model of HIV/AIDS Considering Sexual Preferences Under Antiretroviral Therapy, a case study in the previous works preformed by Espitia is performed. The objective is to gain insight into the qualitative dynamics of the model determining the conditions for the persistence or effective control of the disease in the community through the study of basic properties such as positiveness and boundedness; the calculus of the basic reproduction number; stationary points such as disease-free equilibrium (DFE), boundary equilibrium (BE) and endemic equilibrium (EE); and the local stability (LAS) of disease-free equilibrium. The findings allow us to conclude that the best way to reduce contagion and consequently reach a DFE is thought to be the reduction in the rate of homosexual partners, as they are the most affected population by the virus and are therefore the most likely to become infected and spread it. Increasing the departure rate of infected individuals leads to a decrease in untreated infected heterosexual men and untreated infected women.

Mathematical modeling to study the impact of immigration on the dynamics of the COVID-19 pandemic: A case study for Venezuela.

We propose two different mathematical models to study the effect of immigration on the COVID-19 pandemic. The first model does not consider immigration, whereas the second one does. Both mathematical models consider five different subpopulations: susceptible, exposed, infected, asymptomatic carriers, and recovered. We find the basic reproduction number R0 using the next-generation matrix method for the mathematical model without immigration. This threshold parameter is paramount because it allows us to characterize the evolution of the disease and identify what parameters substantially affect the COVID-19 pandemic outcome. We focus on the Venezuelan scenario, where immigration and emigration have been important over recent years, particularly during the pandemic. We show that the estimation of the transmission rates of the SARS-CoV-2 are affected when the immigration of infected people is considered. This has an important consequence from a public health perspective because if the basic reproduction number is less than unity, we can expect that the SARS-CoV-2 would disappear. Thus, if the basic reproduction number is slightly above one, we can predict that some mild non-pharmaceutical interventions would be enough to decrease the number of infected people. The results show that the dynamics of the spread of SARS-CoV-2 through the population must consider immigration to obtain better insight into the outcomes and create awareness in the population regarding the population flow.

Comparison of the Effective Reproduction Number (Rt) Estimation Methods of COVID-19 Using Simulation Data Based on Available Data from Iran, USA, UK, India, and Brazil.

BACKGROUND: Accurate determination of the effective reproduction number (Rt) is a very important strategy in the epidemiology of contagious diseases, including coronavirus disease 2019 (COVID-19). This study compares different methods of estimating the Rt of susceptible population to identify the most accurate method for estimating Rt. STUDY DESIGN: A secondary study. METHODS: The value of Rt was estimated using attack rate (AR), exponential growth (EG), maximum likelihood (ML), time-dependent (TD), and sequential Bayesian (SB) methods, for Iran, the United States, the United Kingdom, India, and Brazil from June to October 2021. In order to accurately compare these methods, a simulation study was designed using forty scenarios. RESULTS: The lowest mean square error (MSE) was observed for TD and ML methods, with 15 and 12 cases, respectively. Therefore, considering the estimated values of Rt based on the TD method, it was found that Rt values in the United Kingdom (1.33; 95% CI: 1.14-1.52) and the United States (1.25; 95% CI: 1.12-1.38) substantially have been more than those in other countries, such as Iran (1.07; 95% CI: 0.95-1.19), India (0.99; 95% CI: 0.89-1.08), and Brazil (0.98; 95% CI: 0.84-1.14) from June to October 2021. CONCLUSION: The important result of this study is that TD and ML methods lead to a more accurate estimation of Rt of population than other methods. Therefore, in order to monitor and determine the epidemic situation and have a more accurate prediction of the incidence rate, as well as control COVID-19 and similar diseases, the use of these two methods is suggested to more accurately estimate Rt.

The basic reproductive ratio of the 2022 outbreak of the monkey pox virus disease for the United Kingdom, Canada, Brazil, the United Arab Emirates, and Nigeria.

Background: A recent outbreak of the monkey pox virus disease (MPVD) started to spread over the world before the second half of the 2022 year. This outbreak of the monkey pox virus disease is known as the 2022 outbreak of the monkey pox virus disease. The monkey pox virus disease is a type of the pox disease similar to the human one. This disease is an endemic in some African countries; however, a new spreading of this disease started to appear in other countries, such as the Spain, brazil, Greece, the United Kingdom, and Portugal, Australia, and the USA. As of the end of September 2022, the MPVD spread over than 107 countries over the world. Results: This study focuses on the employing of the simplest model of the diseases forecasting which is SIRD model for the finding of the basic reproductive ratio of the monkey pox virus disease in multiple countries over the world where the disease spreads. The model takes into accounts the number of the susceptible people, the number of the infectious people, the number of the recovered people, and the number of the deceased people. Based on the results of the SIRD model coefficients, we find that the basic reproductive ratio values of the recent spreading of the monkey pox virus disease are 1.3274 for the United Kingdom where the first case of the disease was recorded, 1.0714 for the United Arab Emirates, 1.0866 for Nigeria, 1.5589 for Brazil, and 1.3610 for Canada. Conclusions: We find that the average value of the basic reproductive ratio of the 2022 outbreak of the monkey pox virus disease is about 1.2809. This important result of our calculations predicts that the 2022 outbreak of the monkey pox virus disease is turned into pandemic over the world. The things which confirm this result, based on our calculations, are the values of the basic reproductive ratio of the 2022 outbreak of the disease in the considered countries from multiple continents where all the values of the basic reproductive ratio are bigger than one. From this point, the counties over the world must apply multiple procedures for limiting the spreading of the monkey pox virus disease.

Estimating the effective reproduction number for heterogeneous models using incidence data.

The effective reproduction number, R ( t ) , plays a key role in the study of infectious diseases, indicating the current average number of new infections caused by an infected individual in an epidemic process. Estimation methods for the time evolution of R ( t ) , using incidence data, rely on the generation interval distribution, g(τ), which is usually obtained from empirical data or theoretical studies using simple epidemic models. However, for systems that present heterogeneity, either on the host population or in the expression of the disease, there is a lack of data and of a suitable general methodology to obtain g(τ). In this work, we use mathematical models to bridge this gap. We present a general methodology for obtaining explicit expressions of the reproduction numbers and the generation interval distributions, within and between model sub-compartments provided by an arbitrary compartmental model. Additionally, we present the appropriate expressions to evaluate those reproduction numbers using incidence data. To highlight the relevance of such methodology, we apply it to the spread of COVID-19 in municipalities of the state of Rio de Janeiro, Brazil. Using two meta-population models, we estimate the reproduction numbers and the contributions of each municipality in the generation of cases in all others.

Investigating COVID-19 transmission and mortality differences between indigenous and non-indigenous populations in Mexico.

OBJECTIVES: Indigenous populations have been disproportionately affected during pandemics. We investigated COVID-19 mortality estimates among indigenous and non-indigenous populations at national and sub-national levels in Mexico. METHODS: We obtained data from the Ministry of Health, Mexico, on 2,173,036 laboratory-confirmed RT-PCR positive COVID-19 cases and 238,803 deaths. We estimated mortality per 1000 person-weeks, mortality rate ratio (RR) among indigenous vs. non-indigenous groups, and hazard ratio (HR) for COVID-19 deaths across four waves of the pandemic, from February 2020 to March 2022. We also assessed differences in the reproduction number (Rt). RESULTS: The mortality rate among indigenous populations of Mexico was 68% higher than that of non-indigenous groups. Out of 32 federal entities, 23 exhibited higher mortality rates among indigenous groups (P < 0.05 in 13 entities). The fourth wave showed the highest RR (2.40). The crude HR was 1.67 (95% CI: 1.62, 1.72), which decreased to 1.08 (95% CI: 1.04, 1.11) after controlling for other covariates. During the intense fourth wave, the Rt among the two groups was comparable. CONCLUSION: Indigenous status is a significant risk factor for COVID-19 mortality in Mexico. Our findings may reflect disparities in non-pharmaceutical (e.g., handwashing and using facemasks), and COVID-19 vaccination interventions among indigenous and non-indigenous populations in Mexico.