Onset Of Epidemic Research Articles

Monkeypox diagnostic and treatment capacity at epidemic onset: A VACCELERATE online survey

We approached European tertiary care institutions to provide details regarding their management of the current human monkeypox outbreak. 73 out of 105 sites stated to have capacities to manage the outbreak adequately amid the ongoing coronavirus disease 2019 pandemic. There are effective protective measures to prevent nosocomial infections in place at nearly all institutions. Diagnostic and treatment capacities on the other hand have potential to be improved.

Adding a reaction-restoration type transmission rate dynamic-law to the basic SEIR COVID-19 model.

The classical SEIR model, being an autonomous system of differential equations, has important limitations when representing a pandemic situation. Particularly, the geometric unimodal shape of the epidemic curve is not what is generally observed. This work introduces the βSEIR model, which adds to the classical SEIR model a differential law to model the variation in the transmission rate. It considers two opposite thrives generally found in a population: first, reaction to disease presence that may be linked to mitigation strategies, which tends to decrease transmission, and second, the urge to return to normal conditions that pulls to restore the initial value of the transmission rate. Our results open a wide spectrum of dynamic variabilities in the curve of new infected, which are justified by reaction and restoration thrives that affect disease transmission over time. Some of these dynamics have been observed in the existing COVID-19 disease data. In particular and to further exemplify the potential of the model proposed in this article, we show its capability of capturing the evolution of the number of new confirmed cases of Chile and Italy for several months after epidemic onset, while incorporating a reaction to disease presence with decreasing adherence to mitigation strategies, as well as a seasonal effect on the restoration of the initial transmissibility conditions.

Spatial aggregation choice in the era of digital and administrative surveillance data.

Traditional disease surveillance is increasingly being complemented by data from non-traditional sources like medical claims, electronic health records, and participatory syndromic data platforms. As non-traditional data are often collected at the individual-level and are convenience samples from a population, choices must be made on the aggregation of these data for epidemiological inference. Our study seeks to understand the influence of spatial aggregation choice on our understanding of disease spread with a case study of influenza-like illness in the United States. Using U.S. medical claims data from 2002 to 2009, we examined the epidemic source location, onset and peak season timing, and epidemic duration of influenza seasons for data aggregated to the county and state scales. We also compared spatial autocorrelation and tested the relative magnitude of spatial aggregation differences between onset and peak measures of disease burden. We found discrepancies in the inferred epidemic source locations and estimated influenza season onsets and peaks when comparing county and state-level data. Spatial autocorrelation was detected across more expansive geographic ranges during the peak season as compared to the early flu season, and there were greater spatial aggregation differences in early season measures as well. Epidemiological inferences are more sensitive to spatial scale early on during U.S. influenza seasons, when there is greater heterogeneity in timing, intensity, and geographic spread of the epidemics. Users of non-traditional disease surveillance should carefully consider how to extract accurate disease signals from finer-scaled data for early use in disease outbreaks.

Kaolin particle film limits grapevine downy mildew epidemic under open‐field conditions and stimulates the plant defence response

Background and Aims Grapevine downy mildew, the most destructive grapevine disease caused by Plasmopara viticola, requires more environmentally friendly control measures. A chemically inert kaolin particle film (KPF) was tested for the control of downy mildew. Methods and Results Grapevines were pretreated with KPF before P. viticola infection under field conditions, then an epidemic was monitored for 2 years (2020 and 2021). A 6% KPF pretreatment significantly delayed the epidemic onset by 1–2 weeks and decreased the disease index by 30% compared to the untreated vines. The concentration of hydrogen peroxide and malondialdehyde in leaves was reduced by the KPF treatment, indicating mitigation of the oxidative damage caused by P. viticola infection. The protective effect of the application of KPF may be due to the increased activity of antioxidant enzymes and antioxidant metabolites. The activity of disease resistance enzymes and the expression of related genes were also increased by KPF treatment. Conclusions Kaolin particle film reduced grapevine downy mildew and reinforced the defence response of grapevines cultivated in the field. Significance of the Study The positive effect of KPF against downy mildew for grapevines in the field indicated that there is potential to develop KPF for the control of downy mildew.

Regional-based within-year seasonal variations in influenza-related health outcomes across mainland China: a systematic review and spatio-temporal analysis

BackgroundChina experiences large variations in influenza seasonal activity. We aim to update and improve the current understanding of regional-based within-year variations of influenza activity across mainland China to provide evidence for the planning and optimisation of healthcare strategies.MethodsWe conducted a systematic review and spatio-temporal meta-analysis to assess regional-based within-year variations of ILI outpatient consultation rates, influenza test positivity rates amongst both ILI outpatients and SARI inpatients, and influenza-associated excess mortality rates. We searched English and Chinese databases for articles reporting time-series data on the four influenza-related outcomes at the sub-national and sub-annual level. After synthesising the data, we reported on the mean monthly rate, epidemic onset, duration, peak and intensity.ResultsWe included 247 (7.7%) eligible studies in the analysis. We found within-year influenza patterns to vary across mainland China in relation to latitude and geographic location. High-latitude provinces were characterised by having short and intense annual winter epidemics, whilst most mid-latitude and low-latitude provinces experience semi-annual epidemics or year-round activity. Subtype activity varied across the country, with A/H1N1pdm09 and influenza B occurring predominantly in the winter, whereas A/H3N2 activity exhibited a latitudinal divide with high-latitude regions experiencing a winter peak, whilst mid and low-latitude regions experienced a summer epidemic. Epidemic onsets and peaks also varied, occurring first in the north and later in the southeast. We found positive associations between all influenza health outcomes. In addition, seasonal patterns at the prefecture and county-level broadly resembled their wider province.ConclusionsThis is the first systematic review to simultaneously examine the seasonal variation of multiple influenza-related health outcomes at multiple spatial scales across mainland China. The seasonality information provided here has important implications for the planning and optimisation of immunisation programmes and healthcare provision, supporting the need for regional-based approaches to address variations in local epidemiology.

Relative timing of respiratory syncytial virus epidemics in summer 2021 across the United States was similar to a typical winter season.

We used a validated proxy of respiratory syncytial virus (RSV) activity in the United States (Google search data) to evaluate the onsets of RSV epidemics in 2021 and 2016–2019. Despite the unusual out‐of‐season summer timing, the relative timing of RSV epidemics between states in 2021 shared a similar spatial pattern with typical winter RSV seasons. Our results suggest that the onset of RSV epidemics in Florida can serve as a baseline to adjust the initiation of prophylaxis administration and clinical trials in other states regardless of the seasonality of RSV epidemics.

Concordance between the Clinical Diagnosis of Influenza in Primary Care and Epidemiological Surveillance Systems (PREVIGrip Study).

Introduction: Health authorities use different systems of influenza surveillance. Sentinel networks, which are recommended by the World Health Organization, provide information on weekly influenza incidence in a monitored population, based on laboratory-confirmed cases. In Catalonia there is a public website, DiagnostiCat, that publishes the number of weekly clinical diagnoses at the end of each week of disease registration, while the sentinel network publishes its reports later. The objective of this study was to determine whether there is concordance between the number of cases of clinical diagnoses and the number of confirmed cases of influenza, in order to evaluate the predictive potential of a clinical diagnosis-based system. Methods: Population-based ecological time series study in Catalonia. The period runs from the 2010–2011 to the 2018–2019 season. The concordance between the clinical diagnostic cases and the confirmed cases was evaluated. The degree of agreement and the concordance were analysed using Bland–Altman graphs and intraclass correlation coefficients. Results: There was greater concordance between the clinical diagnoses and the sum of the cases confirmed outside and within the sentinel network than between the diagnoses and the confirmed sentinel cases. The degree of agreement was higher when influenza rates were low. Conclusions: There is concordance between the clinical diagnosis and the confirmed cases of influenza. Registered clinical diagnostic cases could provide a good alternative to traditional surveillance, based on case confirmation. Cases of clinical diagnosis of influenza may have the potential to predict the onset of annual influenza epidemics.

Air, bodies, affects and quarantines: containing fear and constructing atmospheres of security and tranquillity

This article will investigate, based on the idea of “atmosphere”, the possibilities raised by the study of the containment of fear in the case of an epidemic in nineteenth-century Marseille. Following Derek P. McCormack, atmospheres will be understood from different and, as will become clear, complementary perspectives. The first is atmosphere understood in its meteorological sense, that is the zone of gaseous material surrounding our bodies and which will be treated here as something that can play a role in the onset and dissemination of epidemics. The second is atmosphere in an affective sense, a set of “shared feelings and moods in a particular space or environment”. Drawing on the contributions of Gernot Böhme on the aesthetics of the reception and production of atmospheres, it will be argued that the atmospheres of security and tranquillity that emerged in Marseille in the presence of epidemic danger were low-intensity atmospheres with the capacity to last over time. Within these atmospheres, elements of a diverse nature were combined, some continuous and others more exceptional, which, taken together, helped create an affective community.

Deconstructing the spatial effects of El Niño and vulnerability on cholera rates in Peru: Wavelet and GIS analyses

This study examined the spatial effects of El Niño and vulnerability on cholera in Peru across the epidemic period of 1991 to 1998. Using Wavelet and GIS analyses, relationships between sea surface temperatures and department-level cholera rates were estimated. In addition, we constructed composite indices to assess spatial vulnerability during the 1997–98 extreme El Niño. The findings demonstrated strong temporal connections in 1997–98, most evident in northern Peru, and less clear connections from 1991–93. Spatially, we found patterns of difference, greater cholera risk in northern coastal Peru in 1997–98, compared to greater risk in central and southern coastal Peru in 1991–92. Overall, the spatial vulnerability analysis suggested preexisting social conditions and disaster impacts increased cholera exposure and infection in 1998. Our study supports the notion that the spatial nature of El Niño's impacts on cholera rates exacerbated cholera vulnerability following the emergence, rather than triggered the epidemic's onset in 1991.

An Outbreak of Severe Kawasaki-Like Disease at the Italian Epicentre of the SARS-CoV-2 Epidemic: An Observational Cohort Study

An outbreak of Kawasaki disease was noted in 2020 in Italy during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. The authors’ objectives in which to evaluate the incidence and features of patients with Kawasaki-like disease diagnosed during the SARS-CoV-2 epidemic. They looked at cases of hemodynamic instability during the acute phase of the disease (Kawasaki disease shock syndrome [KDSS]) and those that might fulfill the criteria of macrophage activation syndrome (MAS).All patients diagnosed with Kawasaki or Kawasaki-like disease who presented over the past 5 years (January 1, 2015, and April 20, 2020) before the COVID pandemic at this institution (Bergamo, Italy) were reviewed and compared with those who were diagnosed after the onset of the pandemic (February 18 to April 20, 2020).Patients with Kawasaki-like presentations were defined according to the 2017 criteria of the American Heart Association, including both classic type and incomplete types. KDSS was defined as Kawasaki disease accompanied by systolic arterial hypotension, a decrease from baseline systolic blood pressure of at least 20%, or the appearance of peripheral hypoperfusion. MAS was defined by using the Pediatric Rheumatology International Trials Organization criteria used in systemic juvenile idiopathic arthritis and commonly used for other systemic autoinflammatory diseases (Kawasaki disease and pediatric systemic lupus erythematosus). Data reviewed from medical records included demographic data, presenting symptoms and treatments, contact with confirmed or suspected cases of coronavirus disease 2019, vital signs, laboratory data (white blood cell count, lymphocyte count, erythrocyte sedimentation rate, C-reactive protein, procalcitonin, ferritin, fibrinogen, pro B-type natriuretic peptide, troponin I, natural killer activity, and interleukin 6 concentrations), electrocardiogram, and echocardiogram data. Current or previous infection with SARS-CoV-2 was obtained by reverse-transcriptase quantitative polymerase chain reaction in nasopharyngeal and oropharyngeal swabs and by serological testing for SARS-CoV-2 immunoglobulin M and immunoglobulin G. Patients were treated with 2 g/kg of intravenous immunoglobulin and aspirin dosed on the basis of Kobayashi score with 2-week methylprednisolone taper.Group 1 (pre–coronavirus disease) comprised 19 patients (7 boys and 12 girls; mean age: 3 years old) and group 2 (during coronavirus disease) included 10 patients (7 boys and 3 girls; mean age: 7.5 years old). A total 8 of 10 patients in group 2 were positive for SARS-CoV-2 immunoglobulin G or immunoglobulin M or both. A total of 5 presented with classic Kawasaki, and 5 presented with incomplete form. Comparing the 2 groups (group 1 versus group 2), there was a significant difference in disease incidence (0.3 patients per month versus 10 patients per month), mean age (3.0 vs 7.5 years), cardiac involvement (2 of 19 vs 6 of 10), KDSS (0 of 19 vs 5 of 10), MAS (0 of 19 vs 5 of 10), and need for adjunctive steroid treatment (3 of 19 vs 8 of 10; all P < .01).A 30-fold increased incidence of Kawasaki-like disease was seen at this institution. Children diagnosed with Kawasaki disease after the SARS-CoV-2 epidemic onset showed evidence of an immune response to the virus, were older, had a higher rate of cardiac involvement, had respiratory and gastrointestinal symptoms, were more resistant to intravenous immunoglobulin treatment, had more severe disease, and had features of MAS.Within the past year, multisystem inflammatory syndrome in children has been diagnosed around the world. Some of these children will fulfill criteria for complete and incomplete Kawasaki. Affected children tend to be older, and there is disproportionate number of Hispanic and African American children. They present with fever, gastrointestinal symptoms, rash, conjunctivitis, mucous membrane involvement, neurocognitive symptoms, lymphadenopathy, and respiratory symptoms. Clinicians should be aware of this syndrome to ensure proper care of patients.

Socioeconomic and environmental patterns behind H1N1 spreading in Sweden

Identifying the critical factors related to influenza spreading is crucial in predicting and mitigating epidemics. Specifically, uncovering the relationship between epidemic onset and various risk indicators such as socioeconomic, mobility and climate factors can reveal locations and travel patterns that play critical roles in furthering an outbreak. We study the 2009 A(H1N1) influenza outbreaks in Sweden’s municipalities between 2009 and 2015 and use the Generalized Inverse Infection Method (GIIM) to assess the most significant contributing risk factors. GIIM represents an epidemic spreading process on a network: nodes correspond to geographical objects, links indicate travel routes, and transmission probabilities assigned to the links guide the infection process. Our results reinforce existing observations that the influenza outbreaks considered in this study were driven by the country’s largest population centers, while meteorological factors also contributed significantly. Travel and other socioeconomic indicators have a negligible effect. We also demonstrate that by training our model on the 2009 outbreak, we can predict the epidemic onsets in the following five seasons with high accuracy.

Major Stressors Favoring Cholera Trigger and Dissemination in Guinea-Bissau (West Africa).

Cholera remains a heavy burden worldwide, especially in Sub-Saharan African countries, which account for the majority of the reported cases on the continent. In this study, a 27-year retrospective analysis of cholera epidemics in Guinea-Bissau was performed in order to highlight major stressors fueling the trigger and dissemination of the disease. Although the role of environmental factors did not always have the same degree of importance for the onset of epidemics, a cholera seasonal pattern was clearly perceived, with most of the reported cases occurring during the wet season. The generated theoretical hypothesis indicated rainfall above climatological average, associated with a lack of WASH (water, sanitation and hygiene) infrastructure, and the occurrence of concomitant epidemics in neighboring countries as the key indicators for optimal conditions for cholera to thrive in Guinea-Bissau. Warmer air temperature, the increase in sea surface temperature, and the decrease in salinity in the coastal areas may also contribute to the emergence and/or aggravation of cholera events. Prediction of the conditions favorable for cholera growth and identification of risk pathways will allow the timely allocation of resources, and support the development of alert tools and mitigation strategies.

Asymmetrical dynamics of epidemic propagation and awareness diffusion in multiplex networks

To better explore asymmetrical interaction between epidemic spreading and awareness diffusion in multiplex networks, we distinguish susceptibility and infectivity between aware and unaware individuals, relax the degree of immunization, and take into account three types of generation mechanisms of individual awareness. We use the probability trees to depict the transitions between distinct states for nodes and then write the evolution equation of each state by means of the microscopic Markovian chain approach (MMCA). Based on the MMCA, we theoretically analyze the possible steady states and calculate the critical threshold of epidemics, related to the structure of epidemic networks, the awareness diffusion, and their coupling configuration. The achieved analytical results of the mean-field approach are consistent with those of the numerical Monte Carlo simulations. Through the theoretical analysis and numerical simulations, we find that global awareness can reduce the final scale of infection when the regulatory factor of the global awareness ratio is less than the average degree of the epidemic network but it cannot alter the onset of epidemics. Furthermore, the introduction of self-awareness originating from infected individuals not only reduces the epidemic prevalence but also raises the epidemic threshold, which tells us that it is crucial to enhance the early warning of symptomatic individuals during pandemic outbreaks. These results give us a more comprehensive and deep understanding of the complicated interaction between epidemic transmission and awareness diffusion and also provide some practical and effective recommendations for the prevention and control of epidemics.

Mitigating bias in estimating epidemic severity due to heterogeneity of epidemic onset and data aggregation

Outbreaks of infectious diseases, such as influenza, are a major societal burden. Mitigation policies during an outbreak or pandemic are guided by the analysis of data of ongoing or preceding epidemics. The reproduction number, R0, defined as the expected number of secondary infections arising from a single individual in a population of susceptibles is critical to epidemiology. For typical compartmental models such as the Susceptible-Infected-Recovered (SIR) R0 represents the severity of an epidemic. It is an estimate of the early-stage growth rate of an epidemic and is an important threshold parameter used to gain insights into the spread or decay of an outbreak. Models typically use incidence counts as indicators of cases within a single large population; however, epidemic data are the result of a hierarchical aggregation, where incidence counts from spatially separated monitoring sites (or sub-regions) are pooled and used to infer R0. Is this aggregation approach valid when the epidemic has different dynamics across the regions monitored? We characterize bias in the estimation of R0 from a merged data set when the epidemics of the sub-regions, used in the merger, exhibit delays in onset. We propose a method to mitigate this bias, and study its efficacy on synthetic data as well as real-world influenza and COVID-19 data.

Historic and bioarchaeological evidence supports late onset of post-Columbian epidemics in Native California

SignificanceThe idea of precipitous continent-wide population decline beginning ca. 1492 has long influenced ecological and social narratives of North America. We analyze the largest systematic dataset of mortality records (n= 33,715 individuals) yet compiled across North America coupling archaeological and historic data to evaluate the nature and timing of Indigenous depopulation in what is now called central California. Findings indicate that mortality risk consistent with a plague-like age-at-death structure began after sustained contact with European invaders ca. AD 1770, suggesting that major depopulation began relatively late in this and possibly other isolated regions of North America. Our analysis does not support a "rebound" in native flora and fauna as a consequence of hypothesized epidemics before the first colonial settlements.

Optimal test-kit-based intervention strategy of epidemic spreading in heterogeneous complex networks.

We propose a deterministic compartmental model of infectious disease that considers the test kits as an important ingredient for the suppression and mitigation of epidemics. A rigorous simulation (with an analytical argument) is provided to reveal the effective reduction of the final outbreak size and the peak of infection as a function of basic reproduction number in a single patch. Furthermore, to study the impact of long and short-distance human migration among the patches, we consider heterogeneous networks where the linear diffusive connectivity is determined by the network link structure. We numerically confirm that implementation of test kits in a fraction of nodes (patches) having larger degrees or betweenness centralities can reduce the peak of infection (as well as the final outbreak size) significantly. A next-generation matrix-based analytical treatment is provided to find out the critical transmission probability in the entire network for the onset of epidemics. Finally, the optimal intervention strategy is validated in two real networks: the global airport network and the transportation network of Kolkata, India.

Inhomogeneous Transmission and Asynchronic Mixing in the Spread of COVID-19 Epidemics

The ongoing epidemic of COVID-19 first found in China has reinforced the need to develop epidemiological models capable of describing the progression of the disease to be of use in the formulation of mitigation policies. Here, this problem is addressed using a metapopulation approach to consider the inhomogeneous transmission of the spread arising from a variety of reasons, like the distribution of local epidemic onset times or of the transmission rates. We show that these contributions can be incorporated into a susceptible-infected-recovered framework through a time-dependent transmission rate. Thus, the reproduction number decreases with time despite the population dynamics remaining uniform and the depletion of susceptible individuals is small. The obtained results are consistent with the early subexponential growth observed in the cumulated number of confirmed cases even in the absence of containment measures. We validate our model by describing the evolution of COVID-19 using real data from different countries, with an emphasis in the case of Mexico, and show that it also correctly describes the longtime dynamics of the spread. The proposed model yet simple is successful at describing the onset and progression of the outbreak, and considerably improves the accuracy of predictions over traditional compartmental models. The insights given here may prove to be useful to forecast the extent of the public health risks of the epidemics, thus improving public policy-making aimed at reducing such risks.

An SIR-type epidemiological model that integrates social distancing as a dynamic law based on point prevalence and socio-behavioral factors

Modeling human behavior within mathematical models of infectious diseases is a key component to understand and control disease spread. We present a mathematical compartmental model of Susceptible–Infectious–Removed to compare the infected curves given by four different functional forms describing the transmission rate. These depend on the distance that individuals keep on average to others in their daily lives. We assume that this distance varies according to the balance between two opposite thrives: the self-protecting reaction of individuals upon the presence of disease to increase social distancing and their necessity to return to a culturally dependent natural social distance that occurs in the absence of disease. We present simulations to compare results for different society types on point prevalence, the peak size of a first epidemic outbreak and the time of occurrence of that peak, for four different transmission rate functional forms and parameters of interest related to distancing behavior, such as: the reaction velocity of a society to change social distance during an epidemic. We observe the vulnerability to disease spread of close contact societies, and also show that certain social distancing behavior may provoke a small peak of a first epidemic outbreak, but at the expense of it occurring early after the epidemic onset, observing differences in this regard between society types. We also discuss the appearance of temporal oscillations of the four different transmission rates, their differences, and how this oscillatory behavior is impacted through social distancing; breaking the unimodality of the actives-curve produced by the classical SIR-model.

Multistage onset of epidemics in heterogeneous networks.

We develop a theory for the susceptible-infected-susceptible (SIS) epidemic model on networks that incorporate both network structure and dynamic correlations. This theory can account for the multistage onset of the epidemic phase in scale-free networks. This phenomenon is characterized by multiple peaks in the susceptibility as a function of the infection rate. It can be explained by that, even under the global epidemic threshold, a hub can sustain the epidemics for an extended period. Moreover, our approach improves theoretical calculations of prevalence close to the threshold in heterogeneous networks and also can predict the average risk of infection for neighbors of nodes with different degree and state on uncorrelated static networks.

Parameters of the Influenza Epidemic in Russia in the 2019-2020 Season

Relevance. The National influenza center uses additional criteria: baselines and thresholds for epidemic intensity for early recognition of the onset and assessment of epidemic intensity. Aim. To characterize the parameters of the flu epidemic in the Russian Federation in the 2019-2020 season and assess the intensity of the last 2 epidemics and the effectiveness of baselines and intensity thresholds. Materials and methods. The database of the research Institute of influenza on weekly morbidity, hospitalization, deaths from influenza and ARVI in the cities-reference bases of the National center for influenza was used. The analysis of the flu situation is based on comparing the current incidence of influenza and ARVI with weekly epic thresholds and baselines. A comparative assessment of the intensity of the last 2 influenza epidemics was carried out using intensity thresholds calculated by the moving epidemic method (MEM). Results. The main parameters of the 2019-20 influenza epidemic compared to the previous one are described. There was an increase in the incidence of clinically diagnosed «fluenza» in the pre-epidemic period and the early onset of both epidemics and reaching a peak in the 6th calendar week. This season, the epidemic started among school children, then among adults, and in the past-among the adult population, the incidence of children 0-2 years old was below the thresholds in both epidemics. This season, the average duration of the epidemic and the incidence in cities were higher among school children and adults, and over the entire period of the epidemic, the incidence was higher in all age groups across the country. This season, the epidemic started in the North Caucasus Federal district, its intensity was medium and low, in the past - it started simultaneously in the Siberian, Ural and North Caucasus districts, and its intensity was very high (in the North Caucasus) and high (in the Siberian Federal district) and the average level of 6 districts. The intensity and duration of epidemics were higher in districts with an early onset (in the North Caucasus and Siberian Federal districts). Both epidemics in the country were of moderate intensity, but there were fewer deaths from influenza in the last epidemic. Conclusion. Comparative characteristics of epidemics using baselines and intensity thresholds allowed us to identify the features of the epidemic process in the seasons 2018-19 and 2019-20: the earlier onset of both epidemics; a noticeable increase in the incidence of fluenza before the beginning of epidemics; a decrease in the intensity of epidemics and mortality from influenza; features of the epidemic process in Federal districts. The effectiveness of using additional criteria in the analysis of the influenza epidemic in Russia and this season has been confirmed. In both epidemics, the weekly epidemic thresholds for influenza and ARVI were more sensitive when determining the start of the epidemic, and the end of it, on the contrary, were baselines, and the baselines of morbidity and hospitalization with a diagnosis «influenza» determined the beginning and end of the epidemic even more precisely.