Average Reproduction Number Research Articles

Evaluation of indoor environmental quality, personal cumulative exposure dose, and aerosol transmission risk levels inside urban buses in Dalian, China.

The transmission of pollutants in buses has an important impact on personal exposure to airborne particles and spread of the COVID-19 epidemic in enclosed spaces. We conducted the following real-time field measurements inside buses: CO2, airborne particle concentration, temperature, and relative humidity data during peak and off-peak hours in spring and autumn. Correlation analysis was adopted to evaluate the dominant factors influencing CO2 and particle mass concentrations in the vehicle. The cumulative personal exposure dose to particulate matter and reproduction number were calculated for passengers on a one-way trip. The results showed the in-cabin CO2 concentrations, with 22.11% and 21.27% of the total time exceeding 1000ppm in spring and autumn respectively. In-cabin PM2.5 mass concentration exceeded 35μm/m3 by 57.35% and 86.42% in spring and autumn, respectively. CO2 concentration and the cumulative number of passengers were approximately linearly correlated in both seasons, with R value up to 0.896. The cumulative number of passengers had the most impact on PM2.5 mass concentration among tested parameters. The cumulative personal exposure dose to PM2.5 during a one-way trip in autumn was up to 43.13μg. The average reproductive number throughout the one-way trip was 0.26; it was 0.57 under the assumed extreme environment. The results of this study provide an important basic theoretical guidance for the optimization of ventilation system design and operation strategies aimed at reducing multi-pollutant integrated health exposure and airborne particle infection (such as SARS-CoV-2) risks.

Estimated incidence and transmission intensity of rubella infection in Zambia pre-vaccine era 2005-2016.

The rubella disease burden in Zambia may be under-estimated. Using models, we describe the transmission dynamics, determine the incidence estimates and assess the level of underestimation of the real burden of rubella infection in Zambia during the pre-vaccination period 2005-2016. This study used both the deterministic compartmental model and likelihood-based method using a Bayesian framework to describe the epidemiology of rubella. A total of 1313 cases of rubella were confirmed with the highest annual number of 255 new cases recorded in 2008. However, 2014 recorded the highest monthly median positivity rate of 9.0%. The observed median rubella cases were 5.5. There was a seasonal pattern in the occurrence of laboratory-confirmed rubella, with higher test positivity rates of rubella infection usually recorded in the months of September, October and November. The modelled monthly median incidence of rubella infection among the general population was 76 and 20 among pregnant women. The incidence of rubella among the non-pregnant women was 44. The average effective reproductive number (Rt) between 2005 and 2016 was estimated as 1.2 with the peak of infection occurring in 2016. The measles surveillance system underestimates the observed burden of rubella. A mass vaccination campaign conducted between January and July is recommended.

First wave COVID-19 pandemic in Senegal: Epidemiological and clinical characteristics.

BackgroundThe novel coronavirus disease 2019 (COVID-19) pandemic has spread from China to the rest of the world. Africa seems less impacted with lower number of cases and deaths than other continents. Senegal recorded its first case on March 2, 2020. We present here data collected from March 2 to October 31, 2020 in Senegal.MethodsSocio-demographic, epidemiological, clinical and virological information were collected on suspected cases. To determine factors associated with diagnosed infection, symptomatic disease and death, multivariable binary logistic regression and log binomial models were used. Epidemiological parameters such as the reproduction number and growth rate were estimated.Results67,608 suspected cases were tested by the IPD laboratories (13,031 positive and 54,577 negative). All age categories were associated with SARS-CoV-2 infection, but also patients having diabetes or hypertension or other cardiovascular diseases. With diagnosed infection, patients over 65 years and those with hypertension and cardiovascular disease and diabetes were highly associated with death. Patients with co-morbidities were associated with symptomatic disease, but only the under 15 years were not associated with. Among infected, 27.67% were asymptomatic (40.9% when contacts were systematically tested; 12.11% when only symptomatic or high-risk contacts were tested). Less than 15 years-old were mostly asymptomatic (63.2%). Dakar accounted for 81.4% of confirmed cases. The estimated mean serial interval was 5.57 (± 5.14) days. The average reproduction number was estimated at 1.161 (95%CI: 1.159–1.162), the growth rate was 0.031 (95%CI: 0.028–0.034) per day.ConclusionsOur findings indicated that factors associated with symptomatic COVID-19 and death are advanced age (over 65 years-old) and comorbidities such as diabetes and hypertension and cardiovascular disease.

The susceptible-infected-recovered-dead model for long-term identification of key epidemiological parameters of COVID-19 in Indonesia

<span>The COVID-19 epidemic has spread massively to almost all countries including Indonesia, in just a few months. An important step to overcoming the spread of the COVID-19 is understanding its epidemiology through mathematical modeling intervention. Knowledge of epidemic dynamics patterns is an important part of making timely decisions and preparing hospitals for the outbreak peak. In this study, we developed the susceptible-infected-recovered-dead (SIRD) model, which incorporates the key epidemiological parameters to model and estimate the long-term spread of the COVID-19. The proposed model formulation is data-based analysis using public COVID-19 data from March 2, 2020 to May 15, 2021. Based on numerical analysis, the spread of the pandemic will begin to fade out after November 5, 2021. As a consequence of this virus attack, the cumulative number of infected, recovered, and dead people were estimated at ≈ 3,200,000, ≈ 3,437,000 and ≈ 63,000 people, respectively. Besides, the key epidemiological parameter indicates that the average reproduction number value of COVID-19 in Indonesia is 7.32. The long-term prediction of COVID-19 in Indonesia and its epidemiology can be well described using the SIRD model. The model can be applied in specific regions or cities in understanding the epidemic pattern of COVID-19.</span>

The Role of Staff in Transmission of SARS-CoV-2 in Long-term Care Facilities.

Background:US long-term care facilities (LTCFs) have experienced a disproportionate burden of COVID-19 morbidity and mortality.Methods:We examined SARS-CoV-2 transmission among residents and staff in 60 LTCFs in Fulton County, Georgia, from March 2020 to September 2021. Using the Wallinga-Teunis method to estimate the time-varying reproduction number, R(t), and linear-mixed regression models, we examined associations between case characteristics and R(t).Results:Case counts, outbreak size and duration, and R(t) declined rapidly and remained low after vaccines were first distributed to LTCFs in December 2020, despite increases in community incidence in summer 2021. Staff cases were more infectious than resident cases (average individual reproduction number, Ri = 0.6 [95% confidence intervals [CI] = 0.4, 0.7] and 0.1 [95% CI = 0.1, 0.2], respectively). Unvaccinated resident cases were more infectious than vaccinated resident cases (Ri = 0.5 [95% CI = 0.4, 0.6] and 0.2 [95% CI = 0.0, 0.8], respectively), but estimates were imprecise.Conclusions:COVID-19 vaccines slowed transmission and contributed to reduced caseload in LTCFs. However, due to data limitations, we were unable to determine whether breakthrough vaccinated cases were less infectious than unvaccinated cases. Staff cases were six times more infectious than resident cases, consistent with the hypothesis that staff were the primary drivers of SARS-CoV-2 transmission in LTCFs.

Assessment of immunization procedures for foot-and-mouth disease in large-scale pig farms in China based on actual data and dynamics

Foot-and-mouth disease (FMD) is an acute, highly infectious and pathogenic animal disease. In recent years, with the rapid development of the swine breeding industry in China, pig farms have shown a trend of larger-scale development. Large-scale pig farms employ standardized management, a high level of automation, and a strict system. However, these farms have a large trading volume, and increased transmission intensity of FMD is noted inside the farm. At present, the main control measure against FMD is pig vaccination. However, a standard for immunization procedures is not available, and currently adopted immunization procedures have not been effectively and systematically evaluated. Taking a typical large-scale pig farm in China as the research subject and considering the breeding pattern, piggery structure, age structure and immunization procedures, an individual-based state probability model is established to evaluate the effectiveness of the immune procedure. Based on numerical simulation, it is concluded that the optimal immunization program involves primary immunization at 40 days of age and secondary immunization at 80 days of age for commercial pigs. Breeding boars and breeding sows are immunized 4 times a year, and reserve pigs are immunized at 169 and 259 days of age. According to the theoretical analysis, the average control reproduction number of individuals under the optimal immunization procedure in the farm is 0.4927. In the absence of immunization, the average is 1.7498, indicating that the epidemic cannot be controlled without immunization procedures.

Dengue epidemiological characteristic in Kuala Lumpur and Selangor, Malaysia

In the last few decades, dengue remains a leading public health problem in Malaysia. The problem persists with increasing trend of cases despite implementation of various interventions by the Ministry of Health Malaysia. The difficulty in eradicating dengue disease throughout the world is related to the complexity of the dengue phenomena. With no access to a safe, effective and affordable dengue vaccine, the only intervention strategy is to control the mosquito vector. In reality, data on mosquitoes is limited, and in most cases, the dynamic of mosquito is unobservable. In this study, based on the dengue incidence data in Malaysia−Kuala Lumpur and Selangor−from the year 2011 to 2014, a transmission model of type SI-SIR is constructed. Transmission rates in the two locations are estimated from the data fitting of the dynamic of infected human to dengue incidence. The average infection rate and the estimated average reproduction number for Selangor are estimated to be 0.1674 week−1 and 1.025, respectively. An enclave in Selangor, the capital city Kuala Lumpur has slightly lower average dengue infection rate of 0.1656 week−1 and estimated average reproduction number of 1.014. Further sensitivity analysis indicated that the dengue infection rate is the critical parameter of dengue transmission in Kuala Lumpur and Selangor. This implies that dengue control strategies should focus on reducing the dengue infection rate. Numerical simulations are also performed to validate the estimated dengue infection rate, to illustrate the dengue dynamics and to determine the effects of dengue control strategies in the two selected locations. The predictive test results of this method also provide good results in capturing the pattern of dengue cases both in Kuala Lumpur and in Selangor. Numerical simulations on instantaneous reproduction number reveal that the instantaneous reproduction number of Kuala Lumpur varies between 0.8 and 1.28 while the instantaneous reproduction number of Selangor varies between 0.7 and 1.2. The insights and results presented in this paper are useful to the relevant authorities in analyzing the effect of mosquito intervention for reducing the prevalence of dengue in Malaysia.

Pandemic fatigue and attenuated impact of avoidance behaviours against COVID-19 transmission in Hong Kong by cross-sectional telephone surveys

ObjectivesThis study aims to explore the attenuated impact of reported avoidance behaviours adherence on the transmission of COVID-19 through cross-sectional surveys in Hong Kong, in order to make up for...

Age-dependency of the Propagation Rate of Coronavirus Disease 2019 Inside School Bubble Groups in Catalonia, Spain.

Background:We analyzed contagions of coronavirus disease 2019 inside school bubble groups in Catalonia, Spain, in the presence of strong nonpharmaceutical interventions from September to December 2020. More than 1 million students were organized in bubble groups and monitored and analyzed by the Health and the Educational departments.Methods:We had access to 2 data sources, and both were employed for the analysis, one is the Catalan school surveillance system and the other of the educational department. As soon as a positive index case is detected by the health system, isolation is required for all members of the bubble group, in addition to a mandatory proactive systematic screening of each individual. All infected cases are reported. It permits the calculation of the average reproductive number (R*), corresponding to the average number of infected individuals per index case.Results:We found that propagation inside of the bubble group was small. Among 75% index cases, there was no transmission to other members in the classroom, with an average R* across all ages inside the bubble of R* = 0.4. We found a significant age trend in the secondary attack rates, with the R* going from 0.2 in preschool to 0.6 in high school youth.Conclusions:The secondary attack rate depends on the school level and therefore on the age. Super-spreading events (outbreaks of 5 cases or more) in childhood were rare, only occurring in 2.5% of all infections triggered from a pediatric index case.

COVID-19 Potential To Infect 4.44% Indian Population

Background: Quantitative characterisation of any contagious diseases could help in making effective strategy to prevent further spreading of the disease. Despite the rapid research in prevention of spreading COVID-19, yet there is no quantitatively information about spatiotemporal epidemiology and epidemic intensity of COVID-19. Therefore, we aimed to quantitatively characterize spatiotemporal epidemiology and epidemic intensity of COVID-19 in Indian geography by determine it’s infectability rate, efficacy rate, transmission rate and likelihood of total populations to be infected with COVID-19 in India. Method: Novel statistical model was designed to quantitatively characterize spatiotemporal epidemiology and epidemic intensity of COVID-19. A Retrospective Cohort study was carried out online and offline in Indian geography using a COVID-19 transmission questionnaires sheet that comprises five mandatory questions. The collected offline as well as online data was then entered into a Microsoft excel sheet to obtain primary data and secondary data required for the mathematical model to calculate quantitative data of spatiotemporal epidemiology and epidemic intensity of COVID-19 in India geography. Result and Discussion: Total 539 responses were analysed in the current study out of 557 received responses between April-2021 to May-2021. Average reproductive number of secondary cases was found 0.77. Infectability rate of COIVD-19 patient was found 38.11% while healthy person has 21.08% risk of becoming infected if they expose to the Primary case. J-Index and H-Index were found 55.32% and 4.44%, respectively, indicating that COVID-19 disease outbreaks as severe epidemic disease in India and has potential to infect ~4.44% Indian population. Conclusion: Higher infectability rate (38.11%) of COIVD-19 patient and higher efficacy rate (21.08%) of healthy individual to be infected with COVID-19 outbreak it a severe epidemic in India. COVID-19 potential to infect about 4.44% India

Outbreak investigation of SARS-CoV-2 transmission in an emergency childcare centre.

ObjectiveTo characterize SARS-CoV-2 transmission following a COVID-19 outbreak in an emergency childcare centre (ECCC) in April 2020 in Quebec, Canada.MethodsThe study population consisted of all the children and employees who attended the ECCC as well as household contacts of the confirmed COVID-19 cases. Of the 120 individuals in the study, five cases were confirmed by epidemiological link and 25 were identified as COVID-19 by RT-PCR among which 19 were analyzed by viral whole genome sequencing. Descriptive epidemiology, social network visualization, and phylogenetic analysis were used to characterize viral transmission.ResultsPhylogenetic analysis identified two separate introductions of distinct lineages of SARS-CoV-2 and estimated an average effective reproductive number of Re = 1.9 (range 0.9–4.9) with a mean doubling time of 3.2 days (range 2.1–5.2). The first and most prevalent lineage was introduced by two asymptomatic children who were likely infected by their parent, a confirmed COVID-19 case working in a long-term care centre. Among infected household adults, attack rates were significantly higher in mothers than in fathers (risk ratio = 4.5; 95% CI 1.1–18.7). The extent of transmission makes it one of the largest documented outbreaks in a daycare in Canada.ConclusionThe analyses carried out showed the probable origin and direction of the transmission of the infection (adult-child, child-adult, and child-child), thus highlighting how asymptomatic children can efficiently transmit SARS-CoV-2.Supplementary InformationThe online version contains supplementary material available at 10.17269/s41997-021-00544-1.

Transmission Dynamics, Heterogeneity and Controllability of SARS-CoV-2: A Rural-Urban Comparison.

Few studies have examined the transmission dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in rural areas and clarified rural–urban differences. Moreover, the effectiveness of non-pharmaceutical interventions (NPIs) relative to vaccination in rural areas is uncertain. We addressed this knowledge gap through using an improved statistical stochastic method based on the Galton–Watson branching process, considering both symptomatic and asymptomatic cases. Data included 1136 SARS-2-CoV infections of the rural outbreak in Hebei, China, and 135 infections of the urban outbreak in Tianjin, China. We reconstructed SARS-CoV-2 transmission chains and analyzed the effectiveness of vaccination and NPIs by simulation studies. The transmission of SARS-CoV-2 showed strong heterogeneity in urban and rural areas, with the dispersion parameters k = 0.14 and 0.35, respectively (k < 1 indicating strong heterogeneity). Although age group and contact-type distributions significantly differed between urban and rural areas, the average reproductive number (R) and k did not. Further, simulation results based on pre-control parameters (R = 0.81, k = 0.27) showed that in the vaccination scenario (80% efficacy and 55% coverage), the cumulative secondary infections will be reduced by more than half; however, NPIs are more effective than vaccinating 65% of the population. These findings could inform government policies regarding vaccination and NPIs in rural and urban areas.

Policy Interventions, Social Distancing, and SARS-CoV-2 Transmission in the United States: A Retrospective State-level Analysis

BackgroundVarious non-pharmaceutical interventions (NPIs) such as stay-at-home orders and school closures have been employed to limit the spread of Coronavirus disease (COVID-19). This study measures the impact of social distancing policies on COVID-19 transmission in US states during the early outbreak phase to assess which policies were most effective. MethodsTo measure transmissibility, we analyze the average effective reproductive number (Rt) in each state the week following its 500th case and doubling time from 500 to 1000 cases. Linear and logistic regressions were performed to assess the impact of various NPIs while controlling for population density, GDP, and certain health metrics. This analysis was repeated for deaths with doubling time to 100 deaths with several healthcare infrastructure control variables. ResultsStates with stay-at-home orders in place at the time of their 500th case were associated with lower average Rt the following week compared to states without them (p<0.001) and significantly less likely to have an Rt>1 (OR 0.07, 95% CI 0.01–0.37, p = 0.004). These states also experienced longer doubling time from 500 to 1000 cases (HR 0.35, 95% CI 0.17–0.72, p = 0.004). States in the highest quartile of average time spent at home were also slower to reach 1000 cases than those in the lowest quartile (HR 0.18, 95% CI 0.06–0.53, p = 0.002). ConclusionsStay-at-home orders had the largest effect of any policy analyzed. Multivariate analyses with cellphone tracking data suggest social distancing adherence drives these effects. States that plan to scale back such measures should carefully monitor transmission metrics.

Epidemiological profile of SARS-CoV-2 among selected regions in Ghana: A cross-sectional retrospective study.

BackgroundGlobal cases of COVID-19 continue to rise, causing havoc to several economies. So far, Ghana has recorded 48,643 confirmed cases with 320 associated deaths. Although summaries of data are usually provided by the Ministry of Health, detailed epidemiological profile of cases are limited. This study sought to describe the socio-demographic features, pattern of COVID-19 spread and the viral load dynamics among subjects residing in northern, middle and part of the southern belt of Ghana.MethodsThis was a cross-sectional retrospective study that reviewed records of samples collected from February to July, 2020. Respiratory specimens such as sputum, deep-cough saliva and nasopharyngeal swabs were collected from suspected COVID-19 subjects in 12 regions of Ghana for laboratory analysis and confirmation by real-time reverse transcription polymerase chain reaction (RT-PCR).ResultsA total of 72,434 samples were collected during the review period, with majority of the sampled individuals being females (37,464; 51.9%). The prevalence of SARS-CoV-2 identified in the study population was 13.2% [95%CI: 12.9, 13.4). Males were mostly infected (4,897; 51.5%) compared to females. Individuals between the ages 21–30 years recorded the highest number of infections (3,144, 33.4%). Symptomatic subjects had higher viral loads (1479.7 copies/μl; IQR = 40.6–178919) than asymptomatic subjects (49.9; IQR = 5.5–3641.6). There was significant association between gender or age and infection with SARS-CoV-2 (p<0.05). Among all the suspected clinical presentations, anosmia was the strongest predictor of SARS-CoV-2 infection (Adj. OR (95%CI): 24.39 (20.18, 29.49). We observed an average reproductive number of 1.36 with a minimum of 1.28 and maximum of 1.43. The virus trajectory shows a gradual reduction of the virus reproductive number.ConclusionThis study has described the epidemiological profile of COVID-19 cases in northern, middle and part of the southern belt of Ghana, with males and younger individuals at greater risk of contracting the disease. Health professionals should be conscious of individuals presenting with anosmia since this was seen as the strongest predictor of virus infection.

Increased Intensity Of PCR Testing Reduced COVID-19 Transmission Within Countries During The First Pandemic Wave.

Experts agree that reverse transcription-polymerase chain reaction (PCR) testing is critical in controlling coronavirus disease 2019 (COVID-19), but decision makers disagree on how much testing is optimal. Controlling for interventions and ecological factors, we used linear regression to quantify testing's impact on COVID-19's average reproduction number, which represents transmissibility, in 173 countries and territories (which account for 99percent of the world's COVID-19 cases) during March-June2020. Among interventions, PCR testing had the greatest influence: a tenfold increase in the ratio of tests to new cases reported reduced the average reproduction number by 9percent across a range of testing levels. Our results imply that mobility reductions (for example, shelter-in-place orders) were less effective in developing countries than in developed countries. Our results help explain how some nations achieved near-elimination of COVID-19 and the failure of lockdowns to slow COVID-19 in others. Our findings suggest that the testing benchmarks used by the World Health Organization and other entities are insufficient for COVID-19 control. Increased testing and isolation may represent the most effective, least costly alternative in terms of money, economic growth, and human life for controlling COVID-19.

What is the reproductive number of yellow fever?

Teaser Our review found the average reproductive number R0 for yellow fever to be 4.81 with a median of 4.21.

COVID-19 early pandemic scenario in India compared to China and rest of the world: a data driven and model analysis

The coronavirus disease 2019 (COVID-19) global pandemic is ongoing, and this is devastating. This study aimed to explore the early COVID-19 pandemic situation in Indian context compared to that of China (the primary epicenter of the ongoing COVID-19 pandemic) as well as in countries outside China. Various epidemiologic parameters, by data driven analysis: basic reproduction number (R0), average reproduction number (R), effective reproduction number (Re), daily growth rate (DGR), case fatality rate (CFR) and case recovery rate (CRR), as well as model analysis: R0, R, Re, serial interval (SI), transmission rate (β) and recovery rate (γ), of COVID-19 early pandemic in India were determined, and were compared with China and rest of the world. The DGR, CRR, CFR, and SI of COVID-19 in India were 17%, 8.25%, 1.87%, and 5.76 days, respectively. The data driven estimates of R0, R and Re were 1.03, 1.73, and 1.35, respectively. The exponential and SIR model had higher estimates of R0, R and Re. The data driven as well as estimated COVID-19 cases reflected the growing nature of the epidemic in India and world excluding China, whereas the same in China revealed the involved population became infected with the disease and moved into the recovered stage. The Re values in India before and after lockdown were 1.62 and 1.37 respectively, with SI 5.52 days and 5.98 days, respectively. The current findings reflected the effectiveness of lockdowns, and therefore, for an early end of the COVID-19 pandemic, strong social distancing is important.

Coupling the Macroscale to the Microscale in a Spatiotemporal Context to Examine Effects of Spatial Diffusion on Disease Transmission.

There are many challenges to coupling the macroscale to the microscale in temporal or spatial contexts. In order to examine effects of an individual movement and spatial control measures on a disease outbreak, we developed a multiscale model and extended the semi-stochastic simulation method by linking individual movements to pathogen’s diffusion, linking the slow dynamics for disease transmission at the population level to the fast dynamics for pathogen shedding/excretion at the individual level. Numerical simulations indicate that during a disease outbreak individuals with the same infection status show the property of clustering and, in particular, individuals’ rapid movements lead to an increase in the average reproduction number R_0, the final size and the peak value of the outbreak. It is interesting that a high level of aggregation the individuals’ movement results in low new infections and a small final size of the infected population. Further, we obtained that either high diffusion rate of the pathogen or frequent environmental clearance lead to a decline in the total number of infected individuals, indicating the need for control measures such as improving air circulation or environmental hygiene. We found that the level of spatial heterogeneity when implementing control greatly affects the control efficacy, and in particular, an uniform isolation strategy leads to low a final size and small peak, compared with local measures, indicating that a large-scale isolation strategy with frequent clearance of the environment is beneficial for disease control.

Estimating the relative probability of direct transmission between infectious disease patients.

Estimating infectious disease parameters such as the serial interval (time between symptom onset in primary and secondary cases) and reproductive number (average number of secondary cases produced by a primary case) are important in understanding infectious disease dynamics. Many estimation methods require linking cases by direct transmission, a difficult task for most diseases. Using a subset of cases with detailed genetic and/or contact investigation data to develop a training set of probable transmission events, we build a model to estimate the relative transmission probability for all case-pairs from demographic, spatial and clinical data. Our method is based on naive Bayes, a machine learning classification algorithm which uses the observed frequencies in the training dataset to estimate the probability that a pair is linked given a set of covariates. In simulations, we find that the probabilities estimated using genetic distance between cases to define training transmission events are able to distinguish between truly linked and unlinked pairs with high accuracy (area under the receiver operating curve value of 95%). Additionally, only a subset of the cases, 10-50% depending on sample size, need to have detailed genetic data for our method to perform well. We show how these probabilities can be used to estimate the average effective reproductive number and apply our method to a tuberculosis outbreak in Hamburg, Germany. Our method is a novel way to infer transmission dynamics in any dataset when only a subset of cases has rich contact investigation and/or genetic data.

Estimating dengue type reproduction numbers for two provinces of Sri Lanka during the period 2013–14

Dengue is an endemic disease in the southeast Asian country Sri Lanka. Two seasonal peaks of dengue incidence were observed every year since 2002 onwards. In this study, we formulate a 2-strain dengue model for analyzing the monthly seasonal dengue incidence data from 2 provinces of Sri Lanka during the period April 2013 to September 2014. The seasonality is incorporated in the model in terms of mosquito biting rate, which we assume to be time periodic. We estimated 2 primary reproduction numbers and the basic reproduction number in a periodic environment using dengue incidence data from the western and the central provinces of Sri Lanka. We also estimated different time-average type reproduction numbers from the model using the data from these 2 provinces. Using univariate sensitivity analysis, we measured the sensitivity of the time average reproduction number () When we vary different parameters of the proposed dengue model, we find the transmission probability of human susceptibility to strain-I infection and the mosquito mortality rate parameters are the most sensitive parameters in dengue transmission in these 2 provinces.