Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAdaptive and optimized COVID-19 vaccination strategies across geographical regions and age groups
152
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We evaluate the efficiency of various heuristic strategies for allocating vaccines against COVID-19 and compare them to strategies found using optimal control theory. Our approach is based on a mathematical model which tracks the spread of disease among different age groups and across different geographical regions, and we introduce a method to combine age-specific contact data to geographical movement data. As a case study, we model the epidemic in the population of mainland Finland utilizing mobility data from a major telecom operator. Our approach allows to determine which geographical regions and age groups should be targeted first in order to minimize the number of deaths. In the scenarios that we test, we find that distributing vaccines demographically and in an age-descending order is not optimal for minimizing deaths and the burden of disease. Instead, more lives could potentially be saved by using strategies which emphasize high-incidence regions and distribute vaccines in parallel to multiple age groups. The level of emphasis that high-incidence regions should be given depends on the overall transmission rate in the population. This observation highlights the importance of updating the vaccination strategy when the effective reproduction number changes due to the general contact patterns changing and new virus variants entering.
rate research
Read More
We present a compartmental meta-population model for the spread of Covid-19 in India. Our model simulates populations at a district or state level using an epidemiological model that is appropriate to Covid-19. Different districts are connected by a transportation matrix developed using available census data. We introduce uncertainties in the testing rates into the model that takes into account the disparate responses of the different states to the epidemic and also factors in the state of the public healthcare system. Our model allows us to generate qualitative projections of Covid-19 spread in India, and further allows us to investigate the effects of different proposed interventions. By building in heterogeneity at geographical and infrastructural levels and in local responses, our model aims to capture some of the complexity of epidemiological modeling appropriate to a diverse country such as India.
Vaccination has been perceived as a key to reaching herd immunity in the current COVID-19 pandemic. This paper examines effectiveness of different vaccination strategies. We investigate the effects of two key elements in mass vaccination, which are allocations and timing of first and second doses and types of vaccines, on the spread of COVID-19. Amid limited supply of approved vaccines and constrained medical resources, the choice of a vaccination strategy is fundamentally an economic problem. We employ standard time-series and panel data models commonly used in economic research with real world data to estimate the effects of progress in vaccination and types of vaccines on health outcomes. Potential confounders such as government responses and peoples behavioral changes are also taken into account. Our findings suggest that the share of people vaccinated with at least one dose is significantly negatively associated with new infections and deaths. Conditioning on first dose progress, full vaccination offers no further reductions in new cases and deaths. For vaccines from China, however, we find weaker effects of vaccination progress on health outcomes. Our results support the extending interval between first and second dose policy adopted by Canada and the UK among others for mRNA-based vaccines. As vaccination progressed, peoples mobility increased and it offset the direct effects of vaccination. Therefore, public health measures are still important to contain the transmission by refraining people from being more mobile after vaccinated.
COVID-19--a viral infectious disease--has quickly emerged as a global pandemic infecting millions of people with a significant number of deaths across the globe. The symptoms of this disease vary widely. Depending on the symptoms an infected person is broadly classified into two categories namely, asymptomatic and symptomatic. Asymptomatic individuals display mild or no symptoms but continue to transmit the infection to otherwise healthy individuals. This particular aspect of asymptomatic infection poses a major obstacle in managing and controlling the transmission of the infectious disease. In this paper, we attempt to mathematically model the spread of COVID-19 in India under various intervention strategies. We consider SEIR type epidemiological models, incorporated with India specific social contact matrix representing contact structures among different age groups of the population. Impact of various factors such as presence of asymptotic individuals, lockdown strategies, social distancing practices, quarantine, and hospitalization on the disease transmission is extensively studied. Numerical simulation of our model is matched with the real COVID-19 data of India till May 15, 2020 for the purpose of estimating the model parameters. Our model with zone-wise lockdown is seen to give a decent prediction for July 20, 2020.
The study carries out predictive modeling based on publicly available COVID-19 data for the duration 01 April to 20 June 2020 pertaining to India and five of its most infected states: Maharashtra, Tamil Nadu, Delhi, Gujarat, and Rajasthan using susceptible, infected, recovered, and dead (SIRD) model. The basic reproduction number R0 is derived by exponential growth method using RStudio package R0. The differential equations reflecting SIRD model have been solved using Python 3.7.4 on Jupyter Notebook platform. For visualization, Python Matplotlib 3.2.1 package is used. The study offers insights on peak-date, peak number of COVID-19 infections, and end-date pertaining to India and five of its states. The results could be leveraged by political leadership, health authorities, and industry doyens for policy planning and execution.
The COVID-19 pandemic has challenged authorities at different levels of government administration around the globe. When faced with diseases of this severity, it is useful for the authorities to have prediction tools to estimate in advance the impact on the health system and the human, material, and economic resources that will be necessary. In this paper, we construct an extended Susceptible-Exposed-Infected-Recovered model that incorporates the social structure of Mar del Plata, the $4^circ$ most inhabited city in Argentina and head of the Municipality of General Pueyrredon. Moreover, we consider detailed partitions of infected individuals according to the illness severity, as well as data of local health resources, to bring these predictions closer to the local reality. Tuning the corresponding epidemic parameters for COVID-19, we study an alternating quarantine strategy, in which a part of the population can circulate without restrictions at any time, while the rest is equally divided into two groups and goes on successive periods of normal activity and lockdown, each one with a duration of $tau$ days. Besides, we implement a random testing strategy over the population. We found that $tau = 7$ is a good choice for the quarantine strategy since it matches with the weekly cycle as it reduces the infected population. Focusing on the health system, projecting from the situation as of September 30, we foresee a difficulty to avoid saturation of ICU, given the extremely low levels of mobility that would be required. In the worst case, our model estimates that four thousand deaths would occur, of which 30% could be avoided with proper medical attention. Nonetheless, we found that aggressive testing would allow an increase in the percentage of people that can circulate without restrictions, being the equipment required to deal with the additional critical patients relatively low.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
