scholarly journals An Age-Structured Mathematical Model of Malaria with Heterogenous Mosquito Biting Pattern

2021 ◽  
Vol 11 (1) ◽  
pp. 9-16
Author(s):  
Ruijun Zhao ◽  
◽  
Sho Kawakami
Keyword(s):  
Mathematical Model ◽  
Age Structured

A mathematical model of tree harvesting in age-structured forests subject to beetle infestations

2017 ◽  
Vol 37 (3) ◽  
pp. 3365-3384 ◽  
Author(s):  
M. C. A. Leite ◽  
B. Chen-Charpentier ◽  
F. B. Agusto
Keyword(s):  
Mathematical Model ◽  
Age Structured ◽  
Tree Harvesting

scholarly journals Unreported cases for Age Dependent COVID-19 Outbreak in Japan

2020 ◽  
Author(s):  
Quentin Griette ◽  
Pierre Magal ◽  
Ousmane Seydi
Keyword(s):  
Mathematical Model ◽  
Age Structure ◽  
Early Stage ◽  
Structured Data ◽  
Computational Method ◽  
Mitigation Measures ◽  
Age Classes ◽  
The Social ◽  
Age Dependent ◽  
Age Structured

AbstractWe investigate the age structured data for the COVID-19 outbreak in Japan. We consider a mathematical model for the epidemic with unreported infectious patient with and without age structure. In particular, we build a new mathematical model and a new computational method to fit the data by using age classes dependent exponential growth at the early stage of the epidemic. This allows to take into account differences in the response of patients to the disease according to their age. This model also allows for a heterogeneous response of the population to the social distancing measures taken by the local government. We fit this model to the observed data and obtain a snapshot of the effective transmissions occurring inside the population at different times, which indicates where and among whom the disease propagates after the start of public mitigation measures.

scholarly journals Age-targeted dose allocation can halve COVID-19 vaccine requirements

2020 ◽  
Author(s):  
Michael T. Meehan ◽  
Daniel G. Cocks ◽  
Jamie M. Caldwell ◽  
James M. Trauer ◽  
Adeshina I. Adekunle ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Severe Disease ◽  
Herd Immunity ◽  
Population Level ◽  
Morbidity And Mortality ◽  
Effective Strategy ◽  
Risk Of Infection ◽  
Middle Aged ◽  
Contact Rates ◽  
Age Structured

ABSTRACTIn anticipation of COVID-19 vaccine deployment, we use an age-structured mathematical model to investigate the benefits of optimizing age-specific dose allocation to suppress the transmission, morbidity and mortality of SARS-CoV-2 and the associated disease, COVID-19. To minimize transmission, we find that the highest priority individuals across 179 countries are typically those between 30 and 59 years of age because of their high contact rates and higher risk of infection and disease. Conversely, morbidity and mortality are initially most effectively reduced by targeting 60+ year olds who are more likely to experience severe disease. However, when population-level coverage is sufficient — such that herd immunity can be achieved through targeted dose allocation — prioritizing middle-aged individuals becomes the most effective strategy to minimize hospitalizations and deaths. For each metric considered, we show that optimizing the allocation of vaccine doses can more than double their effectiveness.

scholarly journals Assessment of Social Distancing for Controlling COVID-19 in Korea: An Age-Structured Modeling Approach

2020 ◽  
Vol 17 (20) ◽  
pp. 7474
Author(s):  
Yongin Choi ◽  
James Slghee Kim ◽  
Heejin Choi ◽  
Hyojung Lee ◽  
Chang Hyeong Lee
Keyword(s):  
Mathematical Model ◽  
Age Groups ◽  
Epidemiological Data ◽  
The Elderly ◽  
Model Parameters ◽  
Social Distancing ◽  
First Case ◽  
Age Structured ◽  
Epidemiological Characteristics ◽  
The Impact

The outbreak of the novel coronavirus disease 2019 (COVID-19) occurred all over the world between 2019 and 2020. The first case of COVID-19 was reported in December 2019 in Wuhan, China. Since then, there have been more than 21 million incidences and 761 thousand casualties worldwide as of 16 August 2020. One of the epidemiological characteristics of COVID-19 is that its symptoms and fatality rates vary with the ages of the infected individuals. This study aims at assessing the impact of social distancing on the reduction of COVID-19 infected cases by constructing a mathematical model and using epidemiological data of incidences in Korea. We developed an age-structured mathematical model for describing the age-dependent dynamics of the spread of COVID-19 in Korea. We estimated the model parameters and computed the reproduction number using the actual epidemiological data reported from 1 February to 15 June 2020. We then divided the data into seven distinct periods depending on the intensity of social distancing implemented by the Korean government. By using a contact matrix to describe the contact patterns between ages, we investigated the potential effect of social distancing under various scenarios. We discovered that when the intensity of social distancing is reduced, the number of COVID-19 cases increases; the number of incidences among the age groups of people 60 and above increases significantly more than that of the age groups below the age of 60. This significant increase among the elderly groups poses a severe threat to public health because the incidence of severe cases and fatality rates of the elderly group are much higher than those of the younger groups. Therefore, it is necessary to maintain strict social distancing rules to reduce infected cases.

scholarly journals Estimating the impact of test-and-treat strategies on hepatitis B virus infection in China by using an age-structured mathematical model

Medicine ◽  
2018 ◽  
Vol 97 (16) ◽  
pp. e0484 ◽  
Author(s):  
Jian Zu ◽  
Miaolei Li ◽  
Guihua Zhuang ◽  
Peifeng Liang ◽  
Fuqiang Cui ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Hepatitis B Virus ◽  
Virus Infection ◽  
Hepatitis B ◽  
Hepatitis B Virus Infection ◽  
Test And Treat ◽  
B Virus ◽  
Age Structured ◽  
The Impact

scholarly journals A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2

Science ◽  
2020 ◽  
Vol 369 (6505) ◽  
pp. 846-849 ◽  
Author(s):  
Tom Britton ◽  
Frank Ball ◽  
Pieter Trapman
Keyword(s):  
Mathematical Model ◽  
Severe Acute Respiratory Syndrome ◽  
Preventive Measures ◽  
Social Activity ◽  
Herd Immunity ◽  
Population Heterogeneity ◽  
Contact Rates ◽  
Mixing Rates ◽  
Age Structured ◽  
Induced Immunity

Despite various levels of preventive measures, in 2020, many countries have suffered severely from the coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Using a model, we show that population heterogeneity can affect disease-induced immunity considerably because the proportion of infected individuals in groups with the highest contact rates is greater than that in groups with low contact rates. We estimate that if R0 = 2.5 in an age-structured community with mixing rates fitted to social activity, then the disease-induced herd immunity level can be ~43%, which is substantially less than the classical herd immunity level of 60% obtained through homogeneous immunization of the population. Our estimates should be interpreted as an illustration of how population heterogeneity affects herd immunity rather than as an exact value or even a best estimate.

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