Infected Population Research Articles

Mathematical Modeling of Cholera Mitigation Incorporating Handwashing

Cholera is a bacterial infection caused by Vibrio cholerae. This bacterium produces a toxin that leads to severe diarrhea and dehydration. Cholera is often associated with areas with poor sanitation, limited access to clean water and overcrowded conditions. Cholera remains a significant global health concern, with outbreaks occurring frequently in areas with poor sanitation and hygiene practices. This study aims to investigate the role of handwashing in mitigating cholera. considering the key factors such as mode of transmission, incubation period, signs and symptoms, treatment measures, risk factors and the main causes of cholera. The study tends to assess the effectiveness of handwashing as a preventive measure against cholera transmission. The research addresses the critical gap in understanding the specific contribution of handwashing in preventing cholera, considering its complex transmission dynamics. The expected results of this study are an association between increased handwashing and a decrease in cholera occurrence. Results from this study will inform evidence-based strategies for disease prevention and contribute to the broader field of infectious disease prevention modeling. The study seeks to elucidate the dynamics of cholera transmission, considering the interplay between susceptible, infectious and recovered population. Analysis of the model shows that there exists a region where the model is mathematically and epidemiologically well posed because its solutions were positive and bounded. Computation of the basic reproduction number, was done using the next generation matrix approach. It was determined that when R0 < 1, cholera does not spread. Stability analysis of the cholera model showed that the disease free equilibrium is both locally and globally asymptotically stable. Ideally, this means that keeping R0 < 1 is a possible strategy for curbing the spread of the disease. Analysis of the endemic equilibrium shows its existence when R0 > 1. Furthermore, the endemic equilibrium is also locally asymptotically stable. This shows that when R0 > 1, the disease persists and spreads in the population. Numerical simulation was done using MATLAB software to show the effectiveness of handwashing on cholera mitigation.

On Stability Properties In Prey-Prodator Model With General Incidence

In this paper we consider a prey-predator systems that the susceptibleand infected prey population are predated by predator species .Also we discuss different models of prey-predator systems With general incidence H ( S,I)The aim of this paper is to study the dynamic of prey-predator model by different techniques with a generalized incidence .

Modeling and investigating malaria P. Falciparum and P. Vivax infections: Application to Djibouti data

Malaria is an infectious and communicable disease, caused by one or more species of Plasmodium parasites. There are five species of parasites responsible for malaria in humans, of which two, Plasmodium Falciparum and Plasmodium Vivax, are the most dangerous. In Djibouti, the two species of Plasmodium are present in different proportions in the infected population: 77% of P. Falciparum and 33% of P. Vivax. In this study we present a new mathematical model describing the temporal dynamics of Plasmodium Falciparum and Plasmodium Vivax co-infection. We focus briefly on the well posedness of this model and on the calculation of the basic reproductive numbers for the infections with each Plasmodium species that help us understand the long-term dynamics of this model (i.e., existence and stability of various eqiuilibria). Then we use computational approaches to: (a) identify model parameters using real data on malaria infections in Djibouti; (b) illustrate the influence of different estimated parameters on the basic reproduction numbers; (c) perform global sensitivity and uncertainty analysis for the impact of various model parameters on the transient dynamics of infectious mosquitoes and infected humans, for infections with each of the Plasmodium species. The originality of this research stems from employing the FAST method and the LHS method to identify the key factors influencing the progression of the disease within the population of Djibouti. In addition, sensitivity analysis identified the most influential parameter for Falciparium and Vivax reproduction rates. Finally, the uncertainty analysis enabled us to understand the variability of certain parameters on the infected compartments.

Control of HPV infection leading to cervical cancer

HPV is the most common sexually transmitted infection and can lead to cancer. Vaccination has been considered as a very effective measure against HPV and along with regular screenings is recommended for the prevention of cervical cancer. In this study we propose and analyze a model for the transmission dynamics of Human Papilloma Virus (HPV). We discuss the optimal vaccination strategy in the case when multiple vaccines are available. We have considered an ODE based compartmental model, incorporating sex structure, we also consider HPV leading to cervical cancer by including pre-cancerous and cancerous compartments in the model. Adding the pre-cancerous and cancerous compartments will help us better understand the role of vaccination in prevention of cancers due to HPV. Using standard techniques from dynamical systems theory, we determine the disease free (DFE) and endemic steady states (EE). We determine a threshold quantity, the basic reproductive number R0 in terms of the model parameters, such that, the DFE is stable when R0<1 and the EE is stable in the case R0>1. The goal of control measures is to try an reduce R0 to be below the threshold value of 1, the regime in which the disease will ultimately be driven to extinction. We also prove the existence of a backward bifurcation in the model, epidemiologically this means that the long time behavior of the model depends on the initial infected population and that the disease may be harder to control, and the condition R0<1 may not suffice to eradicate the disease. Finally, using optimal control theory we consider the effective vaccination strategies using multiple vaccines, the goal is to study the best vaccination strategy using multiple vaccines. Our results show that a higher coverage with vaccines that provide protection against multiple strains, although more expensive, is best suited to control HPV transmission and subsequent development of cancer.

Economic evaluation of two-Strain covid-19 compartmental epidemic model with pharmaceutical and non-pharmaceutical interventions and spatio-temporal patterns

This paper is a novel attempt to analyze different aspects of a two-strain epidemic model. The paper introduces a novel approach to analyzing a two-strain epidemic model, emphasizing the efficacy of combining non-pharmaceutical interventions and vaccination, particularly against new variants. Additionally, it presents a unique spatio-temporal model to assess spatial distribution of infections, offering fresh insights into how spatial factors influence disease transmission and control. The analysis involves stability analysis(local and global) and optimal control. Time-dependent social/non-pharmaceutical interventions coupled with vaccination of the susceptible class in the presence of both strains are analyzed using Pontryagin’s Maximum Principle. The numerical section shows the behavior of the infected class with and without control, the control intensity trend for the scenario when only non-pharmaceutical interventions (NPI) are practiced for the original strain, and when both NPI and vaccination are incorporated with the emergence of the new strain. The evaluation of the Incremental average ratio (IAR) and Incremental cost-effectiveness ratio (ICER) determines that NPI and vaccination as a combination is better and ideal in terms of costs incurred and effectiveness as a whole in averting infection in the presence of a new variant. Finally, we have also proposed a spatio-temporal pattern for the new strain model to analyze patterns using the finite element method by PDE Toolbox to show the effect of different initial conditions and geometry on the density of the infected population.

Dynamics and probability density function of a stochastic COVID-19 epidemic model with nonlinear incidence

In this paper, a stochastic SEIQ model with nonlinear incidence rate is proposed. The existence and uniqueness of global positive solution of the stochastic model are proved. Then, by constructing some Lyapunov functions, we derive a sufficient condition for the ergodic stationary distribution when [Formula: see text] is greater than one. By solving a four-dimensional Fokker–Planck equation, we get the exact expression of log-normal probability density function of stationary distribution for the stochastic model. Sufficient condition for the extinction of the exposed and infected population is also provided. Finally, numerical simulations are presented to verify the above theoretical results.

Dynamical analysis of a modified Leslie–Gower predator–prey model with disease in prey incorporating a prey refuge and treatment

In this paper, we present and assess a predator–prey Leslie–Gower model including disease, refuge and treatment in prey population. There are two groups of prey: those who are susceptible and infected. It is hypothesized that prey population is affected by diseases and refuge, and grows logistically in the absence of predators. Infected prey population receives treatment. The predators’ growth rate is governed by the modified Leslie–Gower dynamics. The dynamical attributes of the resulting system are boundedness, positivity of solutions, extinction criteria, existence and (local and global) stability. Biology uses mathematical analysis to identify the possible attributes of equilibrium points. The focus of this study is to assess how treatment and refuge affect the populations of ill prey, susceptible prey, predators and treated prey. The numerical simulation indicates that the influence of treatment, and refuge change the dynamics of the system (2.1). Extensive numerical simulations were performed to validate our analytical findings by using the Mathematica and MATLAB software.

Leveraging multiple digital footprint datasets to predict racial, sex-based, and sexual-orientation bias across US states

Introduction &amp; BackgroundRacial, gender, and sexual-orientation biases are pervasive throughout society. Importantly, modern digitally oriented datasets can elucidate important societal variables and potential solutions. One contemporary theory that attempts to explain these biases is parasite-stress: an evolutionary psychology hypothesis suggesting that increased infectious diseases rates increase out-group biases. We present preliminary findings that suggest that disease rates are a meaningful geospatial predictor of multiple biases. Objectives &amp; ApproachWe explored biases using geospatial analyses throughout multiple datasets based on US participants: Project Implicit, American National Election Studies (ANES), Google Trends, and Twitter/X. We included state-based variables to compare between states and assess the most important environmental-level predictors of biases. We built generalised linear and linear mixed-effect models and general linear models. Within Project implicit (n &gt; 3,000,000) and ANES datasets (n &gt; 30,000), we assessed racial and sexual-orientation biases via explicit and implicit measures. For Google Trends and Twitter/X datasets, we assessed racial and sex-based biases via search and tweet-per-state scores. To analyse the biases, we included environmental-level variables, e.g., infectious disease rates (developed by Thornhill and Fincher in 2014), and individual-level variables, e.g., political orientation. Relevance to Digital FootprintsThese preliminary findings analyse everyday people’s online behaviour including volunteered surveys, searches and posts. We attempt to address the pressing societal issue of bias by leveraging modern datasets. Our primary goal is to aid policy makers by recommending cost-effective solutions that can improve several factors of the population’s quality of life. ResultsWe find that the most consistently significant predictor of racial bias is infectious disease rates. When leveraging Google Trends data including anti-women terminology, infectious disease rates and population density are consistent predictors of bias. Finally, we find preliminary results suggesting that increased levels of infectious diseases increases homophobic bias. Conclusions &amp; ImplicationsOverall, we find that as infectious disease rates increase in a state, the level of racial and sexist bias significantly increases. Consistent with parasite-stress theory, we argue that focusing on reducing infectious disease rates in an area can have a plethora of benefits including improving physical and mental health and reducing biases that damage society.

Deterministic vs. stochastic dynamics of a predator–prey system with disease in prey: Impact of fear and supplementary foods

In this study, we delve into the dynamics of a generalist predator–prey system with prevalence of a disease in the prey population and the presence of predator-induced fear. In this intricate ecological web, the fear response triggered by predators influences the birth rate of susceptible prey population and also escalates intra-specific competition among them. In an effort to encapsulate a more realistic scenario, we extend our deterministic model to its stochastic counterpart by introducing environmental white noise that impacts the mortality rates of both prey and predator species. Comprehensive mathematical analyses are performed on both the deterministic and stochastic systems to elucidate their qualitative behaviors. Specifically, we derive conditions under which the stochastic system exhibits a unique global positive solution and explore the potential extinction of species within the ecosystem. To unravel the intricate dynamics numerically, we perform sensitivity of parameters, and construct one- and two-parameter bifurcation diagrams. Our investigations reveal that when the costs of fear surpass specific thresholds and disease incidence is high, both the susceptible and infected prey populations face extinction. Notably, the introduction of supplementary foods can lead to system’s destabilization, potentially pushing it towards a state where only predators can persist. Moreover, we observe that lower intensities of white noise have minimal impact on the system’s dynamics, while higher noise intensities introduce substantial fluctuations and may precipitate the extinction of species within the ecosystem. Furthermore, we elucidate the abundances of prey and predator species within the ecosystem through histogram plots.

Modeling the impact of non-human host predation on the transmission of Chagas disease

In addition to the traditional transmission route via the biting-and-defecating process, non-human host predation of triatomines is recognized as another significant avenue for Chagas disease transmission. In this paper, we develop an eco-epidemiological model to investigate the impact of predation on the disease’s spread. Two critical thresholds, Rvp (the basic reproduction number of triatomines) and R0p (the basic reproduction number of the Chagas parasite), are derived to delineate the model’s dynamics. Through the construction of appropriate Lyapunov functions and the application of the Bendixson–Dulac theorem, the global asymptotic stabilities of the equilibria are fully established. The vector-free equilibrium E0 is globally stable when Rvp<1. E1, the disease-free equilibrium, is globally stable when Rvp>1 and R0p<1, while the endemic equilibrium E∗ is globally stable when both Rvp>1 and R0p>1. Numerical simulations highlight that the degree of host predation on triatomines, influenced by non-human hosts activities, can variably increase or decrease the Chagas disease transmission risk. Specifically, low or high levels of host predation can reduce R0p to below unity, while intermediate levels may increase the infected host populations, albeit with a reduction in R0p. These findings highlight the role played by non-human hosts and offer crucial insights for the prevention and control of Chagas disease.

Genetic analysis of SARS-CoV-2 spike gene using Next Generation Sequencing from COVID-19 patients in Erbil/Iraq.

SARS-CoV-2 has been identified by the WHO as a new virus causing mild to severe respiratory illnesses that belong to the Coronavirus family. The virus underwent rapid and continuous changes in the genetic material, especially the S gene, during COVID-19 pandemic and generated a number of new variants announced by WHO in late 2020. Mutations in the S gene have greatly affected virus pathogenesis as the spike protein is responsible for many critical processes. Delta and Omicron variants were studied extensively due to increased mortality and morbidity rates associated with their pandemic waves. This study aimed to analyse the S gene through NGS in an attempt to identify and characterize the circulating variants among the infected population in Erbil/Iraq. Nasopharyngeal and throat swab samples were collected from hospitalized and non-hospitalized patients with COVID-19 symptoms in Erbil City/Iraq from the 1st of November 2021 to the 28th of February 2022. Following confirmation of SARS-CoV-2 infection by RT-PCR, 15 samples were selected and sent to Intergen Lab (Ankara/Turkey) for NGS and analysis. Following analysis and alignment of the received sequences with the Wuhan-Hu-1 strain (wild-type), Delta variant was identified in 13 samples, and Omicron in two. On the whole, different mutation classes have been observed including nonsynonymous, synonymous, non-frameshift deletions and a non-frameshift insertion. The Delta-specific set of mutations, L452R, T478K and P681R, was detected in all Delta isolates. Both Omicron variants appeared to have 35 mutations. D614G variation was conserved in both variants.

Exercise response in post-acute coronary syndrome patients survived to COVID-19 infection

AimsMany studies evaluated the functional response in post-Covid-19 patients; however, they systematically excluded patients with concomitant acute coronary syndrome (ACS).We evaluated the long-term functional capacity assessed by cardiopulmonary exercise test (CPET) in patients hospitalized for ACS and concomitant SARS-CoV2 infection. The secondary aim was to investigate the functional response in patients with symptoms related to “long COVID-19 syndrome” (LCS). MethodsThis cross-sectional case-control study compared 20 patients with ACS and concomitant SARS-COV2 infection with 20 patients without COVID-19. At the follow-up visit (between 6 and 12 months after revascularization procedure) all patients underwent a CPET. ResultsPatients with previous ACS and concomitant COVID-19 showed a reduced O2 consumption than controls (predicted peak V̇O2 74.00% vs 86.70%; p = 0.01) with a high degree of ventilatory inefficiency (VE/ V̇CO2 slope 38.04 vs 30.31; p = 0.002).50% of subjects with previous COVID-19 disease showed symptoms related to “LCS”; this subgroup demarcates the characteristic reduced exercise capacity found in the entire COVID + group. ConclusionsThis study is the first in literature having analyzed the long-term functional capacity phenotype in a population of ACS patients and concomitant SARS-CoV2 infection. Severe ventilatory inefficiency emerged as the functional signature of these patients. Moreover, the subset of patients with symptoms related to LCS has the most compromised long term reduced exercise capacity and an altered ventilation control.

Modified deterministic modeling of Covid-19 in Nigeria: a case of a closed system

Summary In this research a closed system of testing and vaccination is considered using modified deterministic modeling of Covid-19 cases in Nigeria. A disease infection flow transmission diagram was constructed for a model with nine population compartments, represented as SNSVETeQIAISILR, and the assumptions governing the model were presented for the study. A set of nonlinear deterministic differential equations was obtained and tested for positive invariance, positivity of the system solution, boundedness of solution of the equation system, equilibrium point of system stability, endemic equilibrium point, and existence of endemic global stability. The simulated results showed that the equilibrium stability point of the system exists at a basic reproduction number Ro of 0.0000295, and the model estimates show a positive contribution of population recruitment rate (Λ), transmission rate from infected (asymptomatic – β1, symptomatic – β2, undetected but exposed - ф) population, testing rate (βV), (σ), population exposure, exposed tested becoming infected (ρ), quarantine, and isolation to promoting the Covid-19 epidemic infection in Nigeria. Following the findings, the following are recommended: early closure of the country’s borders to check increasing recruitment rate, introduction of social distancing, wearing of nose &amp; mouth masks, early commencement of free testing for the disease (Covid-19), introduction of movement restrictions (close-down/lock-down), compulsory Covid-19 vaccination for every vulnerable person in the population, effective government quarantine and isolation (treatment) centers, and immediate engagement of both medical and non-medical researchers to find lasting solutions.

Stratifying macrophages based on their infectious burden identifies novel host targets for intervention during Crohn's disease associated adherent-invasive Escherichia coli infection.

Bacterial infection is a dynamic process resulting in a heterogenous population of infected and uninfected cells. These cells respond differently based on their bacterial load and duration of infection. In the case of infection of macrophages with Crohn's disease (CD) associated adherent-invasive Escherichia coli (AIEC), understanding the drivers of pathogen success may allow targeting of cells where AIEC replicate to high levels. Here we show that stratifying immune cells based on their bacterial load identifies novel pathways and therapeutic targets not previously associated with AIEC when using a traditional homogeneous infected population approach. Using flow cytometry-based cell sorting we stratified cells into those with low or high intracellular pathogen loads, or those which were bystanders to infection. Immune cells transcriptomics revealed a diverse response to the varying levels of infection while pathway analysis identified novel intervention targets that were directly related to increasing intracellular AIEC numbers. Chemical inhibition of identified targets reduced AIEC intracellular replication or inhibited secretion of tumour necrosis factor alpha (TNFα), a key cytokine associated with AIEC infection. Our results have identified new avenues of intervention in AIEC infection that may also be applicable to CD through the repurposing of already available inhibitors. Additionally, they highlight the applicability of immune cell stratification post-infection as an effective approach for the study of microbial pathogens.

The Analysis and the Solution of Incubation Period in a Disease Model

This study deals with the analysis and the solution of incubation period in a disease model by adopting the mathematical model with incubation period of diseases and the mathematical model without the incubation period of diseases. In the model equations, we partitioned the population into Susceptible (S), Incubated (I), Infected (D) population. We have compared the model equations without incubation period with the model equation with incubation period by solving and incorporating the system of first order linear equations into fourth order Runge-kutta method which has better error accuracy for solving first order equations. Graphical results for incubation class show that the infectious diseases were fatal if immediate attention is not given to endemic villages and communities.Keywords: SID Model, Incubation period, Runge-kutta method, numerical simulation, transmission.

Mathematical Modeling of Infectious Disease and Prey-Predator Interaction with Optimal Control

In this paper, the impact of viral illnesses on the predator-prey relationship with an optimal control analysis is studied. An ecoepidemiological model of four compartments, namely, susceptible prey, susceptible predator, infected prey, and infected predator populations, in the interaction of the prey-predator system is formulated. The fundamental tenet of our ecoepidemiology model is that sick predators do not engage in predation. It is confirmed that the system’s solution exists, is positive, and is bounded. The system’s equilibrium points are determined and computed. Lyapunov functions and a linearizing form are used for local and global stability analysis, respectively. The next generation matrix approach is used to calculate the threshold value for diseased predators and prey at the disease-free equilibrium point. Optimal treatment options for vulnerable and infected populations are established by applying optimal control theory to the ecoepidemiology model of a prey-predator system. MATLAB software is utilized to obtain numerical simulations that validate the analytical outcomes. The optimal control problem simulations demonstrate that the number of infected populations in a given prey-predator system can be decreased by implementing control measures.

ANALYSIS AND OPTIMAL CONTROL OF TUBERCULOSIS DISEASE SPREAD MODEL WITH VACCINATION AND CASE FINDING CONTROL (CASE STUDY: SURABAYA CITY)

Tuberculosis is a contagious disease that infects humans. It is caused by the bacterium Mycobacterium tuberculosis (M.tb). It is the largest infectious disease in the world and has become a major global health problem. Therefore, efforts are being made to control the spread of tuberculosis disease through vaccination and case finding, with the aim of reducing the population of latently infected and actively infected individuals. This study discusses the mathematical model of tuberculosis disease spread, disease-free and endemic equilibrium points, and stability analysis around the equilibrium points. Then, using Pontryagin's minimum principle, the optimal control problem is solved numerically by the 4th-order Runge-Kutta method. Based on the analysis and simulation results, the system is asymptotically stable around the disease-free and endemic equilibrium points. Furthermore, optimal control in the form of vaccination of susceptible individuals is required to further suppress the rate of change of susceptible individuals into latent individuals, while control in the form of case finding on latently infected individuals is required until the 9th year to minimize the population size, while on actively infected individuals, it is required until the 8th year to minimize the population size. Providing optimal control resulted in a 100% increase in the susceptible population and a 100% reduction in the latent and infected populations.

Modelling the Time-varying Transmission of Wild Poliovirus in Pakistan

Aims/Objectives: The efforts to eradicate the wild poliovirus since 1988 have successfully reduced its global prevalence by 99%. However, as of 2024, Pakistan and Afghanistan remain the only two endemic countries facing the virus transmission. This study employs a transmission dynamic model to understand the persistence of wild poliovirus type 1 (WPV1) in Pakistan. Study Design: An ordinary differential equations-based deterministic type of mathematical model was developed. Place and Duration of Study: Department of Epidemiology and Public Health and Institute of Microbiology, University of Agriculture Faisalabad, March 2023 to August 2023. Methodology: We included the number of reported polio cases and the proportion of missed children by supplementary immunization activities (SIAs) across the country from 2017-2022. The model considered both human-human and environment-human virus transmission through sewage contamination and these are represented by time-dependent transmission rate parameters. The parameter estimation was done by model fitting to the reported data of WPV1 cases. The model simulations then predicted the future polio infections in Pakistan. Results: Our analysis identified that the asymptomatic infectious population missed by the SIAs is the major contributor to disease persistence in the country. Moreover, the environment can contribute to virus transmission in areas with poor WASH infrastructure and under-immunized populations. The estimated basic reproduction number through the next-generation matrix method was 1.61 while the estimated effective reproduction number was 0.12. The model showed a better predictive ability than constant transmission rate models. The numerical simulations considering the reduction in the virus-shedding rate by the asymptomatic population resulted in an 85% reduction in future polio cases in Pakistan. Conclusion: The results suggest that endemic virus transmission will continue subject to the current higher vaccination coverage across the country. The model can be further utilized to guide eradication efforts for targeted allocation of preemptive measures.

Stability analysis of fractional epidemic model for two infected classes incorporating hospitalization impact

This article presents an epidemic disease propagation mathematical model in fractional order. The epidemiological characteristics are presented based on the susceptible, exposed, unknown infected, known infected, hospitalized population and the population in the secure zone. Both the disease endemic equilibrium and the disease-free equilibrium’s stability characteristics have been examined using the basic reproduction number. Variation of basic reproduction number based on the different sensitive parameters has been discussed. It has been disputed whether the fractional model provides a uniform, reliable solution. An analysis of the time history of unknown and known infected populations, hospitalized populations and recovered populations at different values of various sensitive parameters has been carried out. To support the key theoretical conclusions, some numerical simulations are completed using MATLAB. The impact of various populations on the propagation of the illness has also been investigated, as well as how specific state variables change over time for various fractional order values.

Salmonellosis in Poland in 2021.

The aim of the article is to present and assess the epidemiological situation of salmonellosis in Poland in 2021, in relation to previous years. The assessment of the epidemiological situation of salmonellosis in Poland was made on the basis of individual data on salmonellosis cases, entered by sanitary-epidemiological stations into the EpiBaza System, data on outbreaks caused by Salmonella bacilli from the Registry of Epidemic Outbreaks System (ROE), as well as on the basis of aggregated data published in the annual bulletins "Infectious Diseases and Poisoning in Poland" (NIPH NIH - NRI, GIS, Warsaw), including information sent by laboratories of sanitary-epidemiological stations, data from the article on the epidemiological situation of salmonellosis in Poland in 2020 and data from the Demographic Research Department of the Central Statistical Office. In 2021, in Poland sanitary-epidemiological stations registered 8,294 cases of salmonellosis - 8,014 cases of intestinal salmonellosis and 280 extra-intestinal salmonellosis, including 190 cases of salmonellosis septicemia. The incidence rate for total salmonellosis was 21.7/100,000 population, for intestinal salmonellosis 21.0, for salmonellosis septicemia 0.50, and 0.23 per 100,000 population for other extra-intestinal infections of salmonellosis etiology. The reported 7,988 cases were classified as confirmed and 306 as probable. There were 5,127 hospitalizations due to salmonellosis, mainly children and the elderly. The peak of the incidence was registered in July. The highest incidence rate of salmonellosis in 2021 was recorded in the Podkarpackie voivodeship (39.8/100,000 population), the lowest in the Świętokrzyskie voivodeship (10.7/100,000 population). The highest incidence of intestinal salmonellosis was registered in the age group 0-4 years, accounting for 44.2% of the total number of cases. Among extra-intestinal infections, almost 62% of cases occurred in people aged 60+. In 2021, sanitary-epidemiological stations were detected and reported 229 outbreaks of food poisoning caused by Salmonella bacilli, 75% of them was Enteritidis serotype. In 2021, the most frequently isolated serotypes were S. Enteritidis 72%, S. Typhimurium (2%) and S. Infantis (0.5%). The serotype was not determined in 24.3% of cases. There were 24 imported cases of salmonellosis from different regions of the world. Due to Salmonella infection 11 people died in 2021. Laboratories of sanitary-epidemiological stations performed 438,183 tests for the presence of Salmonella and Shigella bacilli among humans, 92% of these tests concerned people working in contact with food. In 2021, there was an increase in the number of salmonellosis cases in Poland, compared to 2020. It can therefore be concluded that the COVID-19 pandemic did not have a long-term impact on reducing the number of Salmonella infections. At the same time, despite the increase, the situation of salmonellosis in Poland has not fully returned to the state before the COVID-19 pandemic.The area where we observe a significant difference, is the percentage of hospitalizations, which is the lowest in 2021 since 1998. It can be assumed, that one of the reasons for this, could be a stricter qualification of people with milder symptoms for hospital treatment, in favour of outpatient care.