scholarly journals The effects of flooding and weather conditions on leptospirosis transmission in Thailand

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sudarat Chadsuthi ◽  
Karine Chalvet-Monfray ◽  
Anuwat Wiratsudakul ◽  
Charin Modchang
Keyword(s):  
Sensitivity Analysis ◽  
Public Education ◽  
Mathematical Models ◽  
Disease Transmission ◽  
Transmission Rate ◽  
Weather Conditions ◽  
Transmission Dynamics ◽  
Multiplication Rate ◽  
High Degree ◽  
Transmission Models

AbstractThe epidemic of leptospirosis in humans occurs annually in Thailand. In this study, we have developed mathematical models to investigate transmission dynamics between humans, animals, and a contaminated environment. We compared different leptospire transmission models involving flooding and weather conditions, shedding and multiplication rate in a contaminated environment. We found that the model in which the transmission rate depends on both flooding and temperature, best-fits the reported human data on leptospirosis in Thailand. Our results indicate that flooding strongly contributes to disease transmission, where a high degree of flooding leads to a higher number of infected individuals. Sensitivity analysis showed that the transmission rate of leptospires from a contaminated environment was the most important parameter for the total number of human cases. Our results suggest that public education should target people who work in contaminated environments to prevent Leptospira infections.

scholarly journals The Effect of Seasonal Weather Variation on the Dynamics of the Plague Disease

2017 ◽  
Vol 2017 ◽  
pp. 1-25 ◽  
Author(s):  
Rigobert C. Ngeleja ◽  
Livingstone S. Luboobi ◽  
Yaw Nkansah-Gyekye
Keyword(s):  
Basic Reproduction Number ◽  
Disease Transmission ◽  
Reproduction Number ◽  
Transmission Rate ◽  
Weather Conditions ◽  
Effective Control ◽  
Human Beings ◽  
Weather Variation ◽  
The Basic Reproduction Number ◽  
Seasonal Weather

Plague is a historic disease which is also known to be the most devastating disease that ever occurred in human history, caused by gram-negative bacteria known as Yersinia pestis. The disease is mostly affected by variations of weather conditions as it disturbs the normal behavior of main plague disease transmission agents, namely, human beings, rodents, fleas, and pathogens, in the environment. This in turn changes the way they interact with each other and ultimately leads to a periodic transmission of plague disease. In this paper, we formulate a periodic epidemic model system by incorporating seasonal transmission rate in order to study the effect of seasonal weather variation on the dynamics of plague disease. We compute the basic reproduction number of a proposed model. We then use numerical simulation to illustrate the effect of different weather dependent parameters on the basic reproduction number. We are able to deduce that infection rate, progression rates from primary forms of plague disease to more severe forms of plague disease, and the infectious flea abundance affect, to a large extent, the number of bubonic, septicemic, and pneumonic plague infective agents. We recommend that it is more reasonable to consider these factors that have been shown to have a significant effect on RT for effective control strategies.

scholarly journals Modeling the transmission dynamics of tuberculosis in Khyber Pakhtunkhwa Pakistan

2019 ◽  
Vol 11 (6) ◽  
pp. 168781401985483 ◽  
Author(s):  
Muhammad Altaf Khan ◽  
Manzoor Ahmad ◽  
Saif Ullah ◽  
Muhammad Farooq ◽  
Taza Gul
Keyword(s):  
Sensitivity Analysis ◽  
Disease Transmission ◽  
Transmission Rate ◽  
Model Parameters ◽  
Bacterial Disease ◽  
Treatment Rate ◽  
Khyber Pakhtunkhwa ◽  
Infected People ◽  
Tuberculosis Transmission ◽  
Number Formula

This article addresses the dynamics of the bacterial disease tuberculosis in Khyber Pakhtunkhwa, Pakistan, through a mathematical model. In this work, the latent compartment is divided into slow and fast kinds of progresses. The model is parameterized based on the reported tuberculosis-infected cases in Khyber Pakhtunkhwa for the period 2002–2017. We obtain the basic reproduction number [Formula: see text] of the model using the next-generation method. The estimated value of [Formula: see text] for the given period is approximately 1.38. Furthermore, it is shown that the model has two types of equilibria: disease-free and endemic equilibriums. The global stability analysis of the model equilibria is shown via Lyapunov functions. We also perform the sensitivity analysis of [Formula: see text] and present their corresponding graphical results to examine the relative importance of various model parameters to tuberculosis transmission and prevalence. Finally, some numerical simulations are done for the estimated parameters and the key parameters effects are considered on the curtailing tuberculosis disease. From the numerical results and model sensitivity analysis, it is found that the spread of tuberculosis can be minimized by increasing the treatment rate [Formula: see text] of infected people and decreasing the effective disease transmission rate [Formula: see text] and the rate [Formula: see text] at which the individuals leave treated class reenter infected classes.

scholarly journals Seasonal Variations and Immune Responses: Any Succor for COVID-19 Pandemic in Nigeria

2020 ◽  
Author(s):  
Adeleye Adeshakin ◽  
Oluwamuyiwa Ayanshina ◽  
Lukman Afolabi ◽  
Funmilayo Adeshakin ◽  
Ganiyu Alli-Balogun ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Seasonal Variations ◽  
Disease Transmission ◽  
Weather Conditions ◽  
Transmission Dynamics ◽  
Infectious Disease Transmission ◽  
Healthcare Facilities ◽  
Weather Changes ◽  
The Impact

There is a global rise in the emergence of infectious diseases and the enigmatic coronavirus disease 2019 (COVID-19) being the most recent one. It is ravaging the world with little understanding of its etiology and factors affecting its transmission dynamics. Meanwhile, seasonal variations in weather are major factors impacting infectious disease transmission patterns. Developing countries are likely to be most affected by weather changes, largely because of challenges such as inadequate drainage and sewage management systems, healthcare facilities, education, and funding to efficiently mitigate infectious diseases. In Nigeria, weather conditions alternate between rainy and dry seasons. Conditions such as rainfall, flood, and humidity have been reported to influence infectious disease transmission. Thus, understanding the impact of weather changes in transmission dynamics and immune response to COVID 19 will help in preventive measures and policy making to curtail its spread most especially in Nigeria as the rainy season fully sets in.

scholarly journals Application of Mathematical Modeling in Prediction of COVID-19 Transmission Dynamics

2022 ◽  
Author(s):  
Ali AlArjani ◽  
Md Taufiq Nasseef ◽  
Sanaa M. Kamal ◽  
B. V. Subba Rao ◽  
Mufti Mahmud ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Mathematical Models ◽  
Disease Transmission ◽  
Transmission Dynamics ◽  
New Disease ◽  
Intrinsic Properties ◽  
Future Directions ◽  
Entire World ◽  
Second Wave

AbstractThe entire world has been affected by the outbreak of COVID-19 since early 2020. Human carriers are largely the spreaders of this new disease, and it spreads much faster compared to previously identified coronaviruses and other flu viruses. Although vaccines have been invented and released, it will still be a challenge to overcome this disease. To save lives, it is important to better understand how the virus is transmitted from one host to another and how future areas of infection can be predicted. Recently, the second wave of infection has hit multiple countries, and governments have implemented necessary measures to tackle the spread of the virus. We investigated the three phases of COVID-19 research through a selected list of mathematical modeling articles. To take the necessary measures, it is important to understand the transmission dynamics of the disease, and mathematical modeling has been considered a proven technique in predicting such dynamics. To this end, this paper summarizes all the available mathematical models that have been used in predicting the transmission of COVID-19. A total of nine mathematical models have been thoroughly reviewed and characterized in this work, so as to understand the intrinsic properties of each model in predicting disease transmission dynamics. The application of these nine models in predicting COVID-19 transmission dynamics is presented with a case study, along with detailed comparisons of these models. Toward the end of the paper, key behavioral properties of each model, relevant challenges and future directions are discussed.

Simulating disease transmission dynamics at a multi-scale level

2007 ◽  
Vol 1 (1) ◽  
pp. 26-34 ◽  
Author(s):  
Moshe B Hoshen ◽  
Anthony H Burton ◽  
Themis J V Bowcock
Keyword(s):  
Disease Transmission ◽  
Transmission Dynamics ◽  
Scale Level ◽  
Multi Scale

scholarly journals Identification of effective spreaders in contact networks using dynamical influence

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Ruaridh A. Clark ◽  
Malcolm Macdonald
Keyword(s):  
Disease Transmission ◽  
Average Rate ◽  
Laplacian Matrix ◽  
Disease Spread ◽  
Eigenvector Centrality ◽  
Contact Networks ◽  
Human Contact ◽  
Spread Dynamics ◽  
Definition Of ◽  
High Degree

AbstractContact networks provide insights on disease spread due to the duration of close proximity interactions. For systems governed by consensus dynamics, network structure is key to optimising the spread of information. For disease spread over contact networks, the structure would be expected to be similarly influential. However, metrics that are essentially agnostic to the network’s structure, such as weighted degree (strength) centrality and its variants, perform near-optimally in selecting effective spreaders. These degree-based metrics outperform eigenvector centrality, despite disease spread over a network being a random walk process. This paper improves eigenvector-based spreader selection by introducing the non-linear relationship between contact time and the probability of disease transmission into the assessment of network dynamics. This approximation of disease spread dynamics is achieved by altering the Laplacian matrix, which in turn highlights why nodes with a high degree are such influential disease spreaders. From this approach, a trichotomy emerges on the definition of an effective spreader where, for susceptible-infected simulations, eigenvector-based selections can either optimise the initial rate of infection, the average rate of infection, or produce the fastest time to full infection of the network. Simulated and real-world human contact networks are examined, with insights also drawn on the effective adaptation of ant colony contact networks to reduce pathogen spread and protect the queen ant.

scholarly journals Estimating asymptomatic, undetected and total cases for the COVID-19 outbreak in Wuhan: a mathematical modeling study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xi Huo ◽  
Jing Chen ◽  
Shigui Ruan
Keyword(s):  
Disease Transmission ◽  
Large Scale ◽  
Transmission Rate ◽  
Control Strategies ◽  
Herd Immunity ◽  
Antibody Prevalence ◽  
Screening Process ◽  
Vaccination Programs ◽  
Prevalence Level ◽  
The Impact

Abstract Background The COVID-19 outbreak in Wuhan started in December 2019 and was under control by the end of March 2020 with a total of 50,006 confirmed cases by the implementation of a series of nonpharmaceutical interventions (NPIs) including unprecedented lockdown of the city. This study analyzes the complete outbreak data from Wuhan, assesses the impact of these public health interventions, and estimates the asymptomatic, undetected and total cases for the COVID-19 outbreak in Wuhan. Methods By taking different stages of the outbreak into account, we developed a time-dependent compartmental model to describe the dynamics of disease transmission and case detection and reporting. Model coefficients were parameterized by using the reported cases and following key events and escalated control strategies. Then the model was used to calibrate the complete outbreak data by using the Monte Carlo Markov Chain (MCMC) method. Finally we used the model to estimate asymptomatic and undetected cases and approximate the overall antibody prevalence level. Results We found that the transmission rate between Jan 24 and Feb 1, 2020, was twice as large as that before the lockdown on Jan 23 and 67.6% (95% CI [0.584,0.759]) of detectable infections occurred during this period. Based on the reported estimates that around 20% of infections were asymptomatic and their transmission ability was about 70% of symptomatic ones, we estimated that there were about 14,448 asymptomatic and undetected cases (95% CI [12,364,23,254]), which yields an estimate of a total of 64,454 infected cases (95% CI [62,370,73,260]), and the overall antibody prevalence level in the population of Wuhan was 0.745% (95% CI [0.693%,0.814%]) by March 31, 2020. Conclusions We conclude that the control of the COVID-19 outbreak in Wuhan was achieved via the enforcement of a combination of multiple NPIs: the lockdown on Jan 23, the stay-at-home order on Feb 2, the massive isolation of all symptomatic individuals via newly constructed special shelter hospitals on Feb 6, and the large scale screening process on Feb 18. Our results indicate that the population in Wuhan is far away from establishing herd immunity and provide insights for other affected countries and regions in designing control strategies and planing vaccination programs.

scholarly journals Fail to Yield? An Analysis of Ambulance Crashes in Taiwan

2021 ◽  
Vol 13 (3) ◽  
pp. 1566
Author(s):  
Rong-Chang Jou ◽  
Ming-Che Chao
Keyword(s):  
Public Education ◽  
Weather Conditions ◽  
Medical Emergency ◽  
Transportation Safety ◽  
Emergency Vehicle ◽  
The Road ◽  
Mnl Model ◽  
On The Road ◽  
Crash Injuries ◽  
The Relationship

Introduction—Medical emergency vehicles help patients get to the hospital quickly. However, there were more and more ambulance crashes on the road in Taiwan during the last decade. This study investigated the characteristics of medical emergency vehicle crashes in Taiwan from January 2003 to December 2016. Methods—The ordered logit (OL) model, multinominal logit (MNL) model, and partial proportional odds (PPO) model were applied to investigate the relationship between the severity of ambulance crash injuries and its risk factors. Results—We found the various factors have different effects on the overall severity of ambulance crashes, such as ambulance drivers’ characteristics and road and weather conditions. When another car was involved in ambulance crashes, there was a disproportionate effect on the different overall severity, as found by the PPO model. Conclusions—The results showed that male ambulance drivers and car drivers who failed to yield to an ambulance had a higher risk of severe injury from ambulance crashes. Ambulance crashes are an emerging issue and need further policies and public education regarding Taiwan’s ambulance transportation safety.

scholarly journals Understanding mosquito host-choice behaviour: a new and low-cost method of identifying the sex of human hosts from mosquito blood meals

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Fiona Teltscher ◽  
Sophie Bouvaine ◽  
Gabriella Gibson ◽  
Paul Dyer ◽  
Jennifer Guest ◽  
...  
Keyword(s):  
Blood Meal ◽  
Host Species ◽  
Disease Transmission ◽  
Low Cost ◽  
Host Choice ◽  
Epidemiological Models ◽  
Choice Behaviour ◽  
Vector Borne ◽  
Transmission Models ◽  
Blood Meals

Abstract Background Mosquito-borne diseases are a global health problem, causing hundreds of thousands of deaths per year. Pathogens are transmitted by mosquitoes feeding on the blood of an infected host and then feeding on a new host. Monitoring mosquito host-choice behaviour can help in many aspects of vector-borne disease control. Currently, it is possible to determine the host species and an individual human host from the blood meal of a mosquito by using genotyping to match the blood profile of local inhabitants. Epidemiological models generally assume that mosquito biting behaviour is random; however, numerous studies have shown that certain characteristics, e.g. genetic makeup and skin microbiota, make some individuals more attractive to mosquitoes than others. Analysing blood meals and illuminating host-choice behaviour will help re-evaluate and optimise disease transmission models. Methods We describe a new blood meal assay that identifies the sex of the person that a mosquito has bitten. The amelogenin locus (AMEL), a sex marker located on both X and Y chromosomes, was amplified by polymerase chain reaction in DNA extracted from blood-fed Aedes aegypti and Anopheles coluzzii. Results AMEL could be successfully amplified up to 24 h after a blood meal in 100% of An. coluzzii and 96.6% of Ae. aegypti, revealing the sex of humans that were fed on by individual mosquitoes. Conclusions The method described here, developed using mosquitoes fed on volunteers, can be applied to field-caught mosquitoes to determine the host species and the biological sex of human hosts on which they have blood fed. Two important vector species were tested successfully in our laboratory experiments, demonstrating the potential of this technique to improve epidemiological models of vector-borne diseases. This viable and low-cost approach has the capacity to improve our understanding of vector-borne disease transmission, specifically gender differences in exposure and attractiveness to mosquitoes. The data gathered from field studies using our method can be used to shape new transmission models and aid in the implementation of more effective and targeted vector control strategies by enabling a better understanding of the drivers of vector-host interactions.

scholarly journals Modelling the COVID-19 pandemic in context: an international participatory approach

2020 ◽  
Vol 5 (12) ◽  
pp. e003126
Author(s):  
Ricardo Aguas ◽  
Lisa White ◽  
Nathaniel Hupert ◽  
Rima Shretta ◽  
Wirichada Pan-Ngum ◽  
...  
Keyword(s):  
Mathematical Models ◽  
Low Income ◽  
Scientific Evidence ◽  
Participatory Approach ◽  
Middle Income ◽  
Middle Income Countries ◽  
Containment Measures ◽  
History Of ◽  
High Degree ◽  
The Mathematical Model

The SARS-CoV-2 pandemic has had an unprecedented impact on multiple levels of society. Not only has the pandemic completely overwhelmed some health systems but it has also changed how scientific evidence is shared and increased the pace at which such evidence is published and consumed, by scientists, policymakers and the wider public. More significantly, the pandemic has created tremendous challenges for decision-makers, who have had to implement highly disruptive containment measures with very little empirical scientific evidence to support their decision-making process. Given this lack of data, predictive mathematical models have played an increasingly prominent role. In high-income countries, there is a long-standing history of established research groups advising policymakers, whereas a general lack of translational capacity has meant that mathematical models frequently remain inaccessible to policymakers in low-income and middle-income countries. Here, we describe a participatory approach to modelling that aims to circumvent this gap. Our approach involved the creation of an international group of infectious disease modellers and other public health experts, which culminated in the establishment of the COVID-19 Modelling (CoMo) Consortium. Here, we describe how the consortium was formed, the way it functions, the mathematical model used and, crucially, the high degree of engagement fostered between CoMo Consortium members and their respective local policymakers and ministries of health.

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