scholarly journals Invasion, persistence and control in epidemic models for plant pathogens: the effect of host demography

2009 ◽  
Vol 7 (44) ◽  
pp. 439-451 ◽  
Author(s):  
Nik J. Cunniffe ◽  
Christopher A. Gilligan
Keyword(s):  
Plant Disease ◽  
Plant Pathogens ◽  
Basic Reproductive Number ◽  
Infectious Period ◽  
Reproductive Number ◽  
Generic Model ◽  
Epidemiological Models ◽  
Free Living ◽  
Host Growth ◽  
And Control

Many epidemiological models for plant disease include host demography to describe changes in the availability of susceptible tissue for infection. We compare the effects of using two commonly used formulations for host growth, one linear and the other nonlinear, upon the outcomes for invasion, persistence and control of pathogens in a widely used, generic model for botanical epidemics. The criterion for invasion, reflected in the basic reproductive number, R 0 , is unaffected by host demography: R 0 is simply a function of epidemiological parameters alone. When, however, host growth is intrinsically nonlinear, unexpected results arise for persistence and the control of disease. The endemic level of infection ( I ∞ ) also depends upon R 0 . We show, however, that the sensitivity of I ∞ to changes in R 0 > 1 depends upon which underlying epidemiological parameter is changed. Increasing R 0 by shortening the infectious period results in a monotonic increase in I ∞ . If, however, an increase in R 0 is driven by increases in transmission rates or by decreases in the decay of free-living inoculum, I ∞ first increases ( R 0 < 2), but then decreases ( R 0 > 2). This counterintuitive result means that increasing the intensity of control can result in more endemic infection.

scholarly journals Using proper mean generation intervals in modelling of COVID-19

2021 ◽  
Author(s):  
Xiujuan Tang ◽  
Salihu S Musa ◽  
Shi Zhao ◽  
Daihai He
Keyword(s):  
Time Lag ◽  
Basic Reproductive Number ◽  
Infectious Period ◽  
Reproductive Number ◽  
Secondary Case ◽  
Primary Case ◽  
Control Efficacy ◽  
The Mean ◽  
And Control ◽  
Parameter Values

In susceptible-exposed-infectious-recovered (SEIR) epidemic models, with the exponentially distributed duration of exposed/infectious statuses, the mean generation interval (GI, time lag between infections of a primary case and its secondary case) equals the mean latent period (LP) plus the mean infectious period (IP). It was widely reported that the GI for COVID-19 is as short as 5 days. However, many works in top journals used longer LP or IP with the sum (i.e., GI), e.g., > 7 days. This discrepancy will lead to overestimated basic reproductive number, and exaggerated expectation of infectious attack rate and control efficacy, since all these quantities are functions of basic reproductive number. We argue that it is important to use suitable epidemiological parameter values.

Estimation of the basic reproductive number (R0) for epidemic, highly pathogenic avian influenza subtype H5N1 spread

2008 ◽  
Vol 137 (2) ◽  
pp. 219-226 ◽  
Author(s):  
M. P. WARD ◽  
D. MAFTEI ◽  
C. APOSTU ◽  
A. SURU
Keyword(s):  
Avian Influenza ◽  
Highly Pathogenic Avian Influenza ◽  
Control Strategies ◽  
Basic Reproductive Number ◽  
Infectious Period ◽  
Reproductive Number ◽  
Highly Pathogenic ◽  
Pathogenic Avian Influenza ◽  
True Basic ◽  
Road Distance

SUMMARYThree different methods were used for estimating the basic reproductive number (R0) from data on 110 outbreaks of highly pathogenic avian influenza (HPAI) subtype H5N1 that occurred in village poultry in Romania, 12 May to 6 June 2006. We assumed a village-level infectious period of 7 days. The methods applied were GIS-based identification of nearest infectious neighbour (based on either Euclidean or road distance), the method of epidemic doubling time, and a susceptible–infectious (SI) modelling approach. In general, the estimated basic reproductive numbers were consistent: 2·14, 1·95, 2·68 and 2·21, respectively. Although the true basic reproductive number in this epidemic is unknown, results suggest that the use of a range of methods might be useful for characterizing epidemics of infectious diseases. Once the basic reproductive number has been estimated, better control strategies and targeted surveillance programmes can be designed.

scholarly journals Variation in SARS-CoV-2 free-living survival and environmental transmission can modulate the intensity of COVID-19 outbreaks

2020 ◽  
Author(s):  
C. Brandon Ogbunugafor ◽  
Miles Miller-Dickson ◽  
Victor A. Meszaros ◽  
Lourdes M. Gomez ◽  
Anarina L. Murillo ◽  
...  
Keyword(s):  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Disease Dynamics ◽  
Free Living ◽  
Environmental Transmission ◽  
Country Level ◽  
History Of ◽  
Virus Survival ◽  
Case Data

ABSTRACTCOVID-19 has circled the globe, rapidly expanding into a pandemic within a matter of weeks. While early studies revealed important features of SARS-CoV-2 transmission, the role of variation in free-living virus survival in modulating the dynamics of outbreaks remains unclear and controversial. Using an empirically determined understanding of the natural history of SARS-CoV-2 infection and detailed, country-level case data, we elucidate how variation in free-living virus survival influences key features of COVID-19 epidemics. Our findings suggest that environmental transmission can have a subtle, yet significant influence on COVID-19’s basic reproductive number () and other key signatures of outbreak intensity. Summarizing, we propose that variation in environmental transmission may explain some observed differences in disease dynamics from setting to setting, and can inform public health interventions.

scholarly journals Estimation of the Outbreak Severity and Evaluation of Epidemic Prevention Ability of COVID-19 by Province in China

2020 ◽  
Vol 110 (12) ◽  
pp. 1837-1843
Author(s):  
Yilei Ma ◽  
Xuehan Liu ◽  
Weiwei Tao ◽  
Yuchen Tian ◽  
Yanran Duan ◽  
...  
Keyword(s):  
Emergency Response ◽  
Disease Transmission ◽  
Initial Period ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Linear Mixed Effects Model ◽  
Linear Mixed Effects ◽  
Imported Cases ◽  
And Control ◽  
Case Data

Objectives. To compare the epidemic prevention ability of COVID-19 of each province in China and to evaluate the existing prevention and control capacity of each province. Methods. We established a quasi-Poisson linear mixed-effects model using the case data in cities outside Wuhan in Hubei Province, China. We adapted this model to estimate the number of potential cases in Wuhan and obtained epidemiological parameters. We estimated the initial number of cases in each province by using passenger flowrate data and constructed the extended susceptible–exposed–infectious–recovered model to predict the future disease transmission trends. Results. The estimated potential cases in Wuhan were about 3 times the reported cases. The basic reproductive number was 3.30 during the initial outbreak. Provinces with more estimated imported cases than reported cases were those in the surrounding provinces of Hubei, including Henan and Shaanxi. The regions where the number of reported cases was closer to the predicted value were most the developed areas, including Beijing and Shanghai. Conclusions. The number of confirmed cases in Wuhan was underestimated in the initial period of the outbreak. Provincial surveillance and emergency response capabilities vary across the country.

scholarly journals Clinical and Epidemiological Aspects of Diphtheria: A Systematic Review and Pooled Analysis

2019 ◽  
Vol 71 (1) ◽  
pp. 89-97 ◽  
Author(s):  
Shaun A Truelove ◽  
Lindsay T Keegan ◽  
William J Moss ◽  
Lelia H Chaisson ◽  
Emilie Macher ◽  
...  
Keyword(s):  
Healthcare Access ◽  
Credible Interval ◽  
Pooled Analysis ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Knowledge Gaps ◽  
Symptomatic Disease ◽  
Childhood Morbidity ◽  
Duration Of Infection ◽  
And Control

Abstract Background Diphtheria, once a major cause of childhood morbidity and mortality, all but disappeared following introduction of diphtheria vaccine. Recent outbreaks highlight the risk diphtheria poses when civil unrest interrupts vaccination and healthcare access. Lack of interest over the last century resulted in knowledge gaps about diphtheria’s epidemiology, transmission, and control. Methods We conducted 9 distinct systematic reviews on PubMed and Scopus (March–May 2018). We pooled and analyzed extracted data to fill in these key knowledge gaps. Results We identified 6934 articles, reviewed 781 full texts, and included 266. From this, we estimate that the median incubation period is 1.4 days. On average, untreated cases are colonized for 18.5 days (95% credible interval [CrI], 17.7–19.4 days), and 95% clear Corynebacterium diphtheriae within 48 days (95% CrI, 46–51 days). Asymptomatic carriers cause 76% (95% confidence interval, 59%–87%) fewer cases over the course of infection than symptomatic cases. The basic reproductive number is 1.7–4.3. Receipt of 3 doses of diphtheria toxoid vaccine is 87% (95% CrI, 68%–97%) effective against symptomatic disease and reduces transmission by 60% (95% CrI, 51%–68%). Vaccinated individuals can become colonized and transmit; consequently, vaccination alone can only interrupt transmission in 28% of outbreak settings, making isolation and antibiotics essential. While antibiotics reduce the duration of infection, they must be paired with diphtheria antitoxin to limit morbidity. Conclusions Appropriate tools to confront diphtheria exist; however, accurate understanding of the unique characteristics is crucial and lifesaving treatments must be made widely available. This comprehensive update provides clinical and public health guidance for diphtheria-specific preparedness and response.

scholarly journals Population dynamics of botanical epidemics involving primary and secondary infection

1997 ◽  
Vol 352 (1353) ◽  
pp. 591-608 ◽  
Author(s):  
Christopher A. Gilligan ◽  
Adam Kleczkowski
Keyword(s):  
Fungal Infection ◽  
Mycorrhizal Fungi ◽  
Plant Pathogens ◽  
Initial Conditions ◽  
Secondary Infection ◽  
Generic Model ◽  
Direct Transmission ◽  
Initial Inoculum ◽  
Special Cases ◽  
Host Growth

In this paper we study the dynamical properties of models for botanical epidemics, especially for soil–borne fungal infection. The models develop several new concepts, involving dual sources of infection, host and inoculum dynamics. Epidemics are modelled with respect to the infection status of whole plants and plant organs (the G model) or to lesion density and size (the SW model). The infection can originate in two sources, either from the initial inoculum (primary infection) or by a direct transmission between plant tissue (secondary infection). The first term corresponds to the transmission through the free–living stages of macroparasites or an external source of infection in certain medical models, whereas the second term is equivalent to direct transmission between the hosts in microparasitic infections. The models allow for dynamics of host growth and inoculum decay. We show that the two models for root and lesion dynamics can be derived as special cases of a single generic model. Analytical and numerical methods are used to analyse the behaviour of the models for static, unlimited (exponential) and asymptotically limited host growth with and without secondary infection, and with and without decay of initial inoculum. The models are shown to exhibit a range of epidemic behaviour within single seasons that extends from simple monotonic increase with saturation of the host population, through temporary plateaux as the system switches from primary to secondary infection, to effective elimination of the pathogen by the host outgrowing the fungal infection. For certain conditions, the equilibrium values are shown to depend on initial conditions. These results have important consequences for the control of plant disease. They can be applied beyond soil–borne plant pathogens to mycorrhizal fungi and aerial pathogens while the principles of primary and secondary infection with host and inoculum dynamics may be used to link classical models for both microparasitic and macroparasitic infections.

scholarly journals Macro-parasite transmission in dynamic seasonal environment: Basic Reproductive Number, endemicity, and control

2019 ◽  
Author(s):  
Q. Huang ◽  
D. Gurarie ◽  
M. Ndeffo-Mbah ◽  
E. Li ◽  
CH. King
Keyword(s):  
Intermediate Host ◽  
Worm Burden ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Optimal Time ◽  
Optimal Timing ◽  
Snail Population ◽  
Seasonal Environment ◽  
Combined Strategy ◽  
And Control

AbstractSeasonality of transmission environment, which includes snail populations and habitats, or human-snail contact patterns, can affect the dynamics of schistosomiasis infection, and control outcomes. Conventional modeling approaches often ignore or oversimplify it by applying ‘seasonal mean’ formulation. Mathematically, such ‘averaging’ is justified when model outputs/quantities of interest depend linearly on input variables. That is not generally the case for macroparasite transmission models, where model outputs are nonlinear functions of seasonality fashion.Another commonly used approach for Schistosomiasis modeling is a reduction of coupled human-snail system to a single ‘human equation’, via quasi-stationary snail (intermediate host) dynamics. The basic questions arising from these approaches are whether such ‘seasonal averaging’ and ‘intermediate host reduction’ are suitable for highly variable/seasonal environments, and what implications these methods have on models’ predictive potential of control interventions.Here we address these questions by using a combination of mathematical analysis and numerical simulation of two commonly used models for macroparasite transmission, MacDonald (MWB), and stratified worm burden (SWB) snail-human systems. We showed that predictions from ‘seasonal averaging’ models can depart significantly from those of quasi-stationary models. Typically, seasonality would lower endemicity and sustained infection, vs. stationary system with comparable transmission inputs. Furthermore, discrepancies between the two models (‘seasonal’ and its ‘stationary mean’) increase with amplitude (or variance) of seasonality. So sufficiently high variability can render infection unsustainable. Similar discrepancies were observed between coupled and reduced ‘single host’ models, with reduced model overpredicting sustained endemicity. Seasonal variability of transmission raises the question of optimal control timing. Using dynamic simulation, we show that optimal timing of repeated MDA is about half season past the snail peak, where snail population attains its minimal value. Compared to sub-optimal timing, such strategy can reduce human worm burden by factor 2 after 5-6 rounds of MDA. We also extended our models for dynamic snail populations, which allowed us to study the effect of repeated molluscicide, or combined strategy (MDA + molluscicide). The optimal time for molluscicide alone is the end or the start of season, and combined strategy can give additional reduction, and in some cases lead to elimination.Overall, reduced sustainability in seasonal environment makes it more amenable to control interventions, compared to stationary environment.

scholarly journals Exploring the potential effect of COVID-19 on an endangered great ape

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fernando Colchero ◽  
Winnie Eckardt ◽  
Tara Stoinski
Keyword(s):  
Biodiversity Loss ◽  
Potential Effect ◽  
Basic Reproductive Number ◽  
Economic Losses ◽  
Reproductive Number ◽  
Great Ape ◽  
Epidemiological Models ◽  
Loss Of Life ◽  
Probability Of Extinction ◽  
Vaccination Campaigns

AbstractThe current COVID-19 pandemic has created unmeasurable damages to society at a global level, from the irreplaceable loss of life, to the massive economic losses. In addition, the disease threatens further biodiversity loss. Due to their shared physiology with humans, primates, and particularly great apes, are susceptible to the disease. However, it is still uncertain how their populations would respond in case of infection. Here, we combine stochastic population and epidemiological models to simulate the range of potential effects of COVID-19 on the probability of extinction of mountain gorillas. We find that extinction is sharply driven by increases in the basic reproductive number and that the probability of extinction is greatly exacerbated if the immunity lasts less than 6 months. These results stress the need to limit exposure of the mountain gorilla population, the park personnel and visitors, as well as the potential of vaccination campaigns to extend the immunity duration.

scholarly journals Insight into Malaria Transmission and Control in Endemic Areas

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Beatty V. Maikai ◽  
Jarlath U. Umoh ◽  
Victor A. Maikai
Keyword(s):  
Malaria Control ◽  
Control Program ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
The Status ◽  
Health Concept ◽  
Endemic Areas ◽  
The One ◽  
And Control ◽  
State Regional

The global effort of malaria control is in line with the one world one health concept, but then a globally defined (one-size-fits-all) malaria control strategy would be inefficient. A model was used to examine the likely impact of malaria parasite interventions for a steady state regional control program in endemic areas. Assumptions varied about two targeted epidemiologic control points on the basic reproductive number, Ro, which is affected by different factors and upon which the status of malaria in any community will depend. For any effective malaria control and eradication program, environmental and socio-economic factors should also be considered.

scholarly journals Characteristics and evaluation of the effectiveness of monitoring and control measures for the first 69 Patients with COVID-19 from 18 January 2020 to 2 March in Wuxi, China

2020 ◽  
Author(s):  
Ping Shi ◽  
Yumeng Gao ◽  
Yuan Shen ◽  
Enping Chen ◽  
Hai Chen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Close Contact ◽  
Virus Detection ◽  
Control Measures ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Monitoring And Control ◽  
Infected People ◽  
Novel Coronavirus ◽  
And Control

Abstract Background: The novel coronavirus disease 2019(COVID-19) outbreak and has caused has caused 82,830 confirmed cases and 4,633 deaths in China by 26 April 2020. We analyzed data on 69 infections in Wuxi to describe the epidemiologic characteristics and evaluate the control measures.Methods: The demographic characteristics, exposure history, and illness timelines of COVID-19 cases in Wuxi were collected.Results: Among the 69 positive infections with COVID-19, mild and normal types accounted for 75.36% (52/69), adolescents and children are mainly mild and asymptomatic. The basic reproductive number was estimated to be 1.12 (95% CI, 0.71 to 1.69). The mean incubation period was estimated to be 4.77 days (95% CI, 3.61 to 5.94), with a mean serial interval of 6.31 days (95%CI, 5.12 to 7.50). We also found that age (RR=1.57, 95%CI: 1.11-2.21) and fever (RR=4.09, 95%CI: 1.10-15.19) were risk factors for COVID-19 disease severity.Conclusions: The incidence of COVID-19 in Wuxi has turned into a lower level, suggesting that the early prevention and control measures have achieved effectiveness. The community transmission can be effectively prevented through isolation and virus detection of all the people who were exposed together and close contact with the infected people. Aging and fever are risk factors for clinical outcome, which might be useful for preventing severe transition.

Sign in / Sign up

Export Citation Format

Share Document