The Impact of LoRa Transmission Parameters on Packet Delivery and Dissipation Power

2021 ◽  
Author(s):  
Tamara Rasic ◽  
Joao Lucas Eberl Simon ◽  
Nenad Zoric ◽  
Mitar Simic
Keyword(s):  
Packet Delivery ◽  
Transmission Parameters ◽  
Dissipation Power ◽  
The Impact

scholarly journals Estimation of the transmission dynamics of African swine fever virus within a swine house

2017 ◽  
Vol 145 (13) ◽  
pp. 2787-2796 ◽  
Author(s):  
J. P. NIELSEN ◽  
T. S. LARSEN ◽  
T. HALASA ◽  
L. E. CHRISTIANSEN
Keyword(s):  
African Swine Fever Virus ◽  
African Swine Fever ◽  
Simulation Models ◽  
Fever Virus ◽  
Economic Consequences ◽  
Estimation Of Parameters ◽  
Swine Industry ◽  
Transmission Parameters ◽  
The Individual ◽  
The Impact

SUMMARYThe spread of African swine fever virus (ASFV) threatens to reach further parts of Europe. In countries with a large swine production, an outbreak of ASF may result in devastating economic consequences for the swine industry. Simulation models can assist decision makers setting up contingency plans. This creates a need for estimation of parameters. This study presents a new analysis of a previously published study. A full likelihood framework is presented including the impact of model assumptions on the estimated transmission parameters. As animals were only tested every other day, an interpretation was introduced to cover the weighted infectiousness on unobserved days for the individual animals (WIU). Based on our model and the set of assumptions, the within- and between-pen transmission parameters were estimated to βw = 1·05 (95% CI 0·62–1·72), βb = 0·46 (95% CI 0·17–1·00), respectively, and the WIU = 1·00 (95% CI 0–1). Furthermore, we simulated the spread of ASFV within a pig house using a modified SEIR-model to establish the time from infection of one animal until ASFV is detected in the herd. Based on a chosen detection limit of 2·55% equivalent to 10 dead pigs out of 360, the disease would be detected 13–19 days after introduction.

scholarly journals Incubation periods impact the spatial predictability of cholera and Ebola outbreaks in Sierra Leone

2020 ◽  
Vol 117 (9) ◽  
pp. 5067-5073 ◽  
Author(s):  
Rebecca Kahn ◽  
Corey M. Peak ◽  
Juan Fernández-Gracia ◽  
Alexandra Hill ◽  
Amara Jambai ◽  
...  
Keyword(s):  
Sierra Leone ◽  
Incubation Period ◽  
Population Connectivity ◽  
Disease Spread ◽  
Long Distance ◽  
Spatial Spread ◽  
Transmission Parameters ◽  
Incubation Periods ◽  
Using Data ◽  
The Impact

Forecasting the spatiotemporal spread of infectious diseases during an outbreak is an important component of epidemic response. However, it remains challenging both methodologically and with respect to data requirements, as disease spread is influenced by numerous factors, including the pathogen’s underlying transmission parameters and epidemiological dynamics, social networks and population connectivity, and environmental conditions. Here, using data from Sierra Leone, we analyze the spatiotemporal dynamics of recent cholera and Ebola outbreaks and compare and contrast the spread of these two pathogens in the same population. We develop a simulation model of the spatial spread of an epidemic in order to examine the impact of a pathogen’s incubation period on the dynamics of spread and the predictability of outbreaks. We find that differences in the incubation period alone can determine the limits of predictability for diseases with different natural history, both empirically and in our simulations. Our results show that diseases with longer incubation periods, such as Ebola, where infected individuals can travel farther before becoming infectious, result in more long-distance sparking events and less predictable disease trajectories, as compared to the more predictable wave-like spread of diseases with shorter incubation periods, such as cholera.

scholarly journals Heterogeneity in transmission parameters of hookworm infection within the baseline data from the TUMIKIA study in Kenya

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
James E. Truscott ◽  
Alison K. Ower ◽  
Marleen Werkman ◽  
Katherine Halliday ◽  
William E. Oswald ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Positive Association ◽  
Monitoring And Evaluation ◽  
Host Population ◽  
Transmission Model ◽  
High Coverage ◽  
Study Region ◽  
Transmission Parameters ◽  
Parasite Aggregation ◽  
The Impact

Abstract Background As many countries with endemic soil-transmitted helminth (STH) burdens achieve high coverage levels of mass drug administration (MDA) to treat school-aged and pre-school-aged children, understanding the detailed effects of MDA on the epidemiology of STH infections is desirable in formulating future policies for morbidity and/or transmission control. Prevalence and mean intensity of infection are characterized by heterogeneity across a region, leading to uncertainty in the impact of MDA strategies. In this paper, we analyze this heterogeneity in terms of factors that govern the transmission dynamics of the parasite in the host population. Results Using data from the TUMIKIA study in Kenya (cluster STH prevalence range at baseline: 0–63%), we estimated these parameters and their variability across 120 population clusters in the study region, using a simple parasite transmission model and Gibbs-sampling Monte Carlo Markov chain techniques. We observed great heterogeneity in R0 values, with estimates ranging from 1.23 to 3.27, while k-values (which vary inversely with the degree of parasite aggregation within the human host population) range from 0.007 to 0.29 in a positive association with increasing prevalence. The main finding of this study is the increasing trend for greater parasite aggregation as prevalence declines to low levels, reflected in the low values of the negative binomial parameter k in clusters with low hookworm prevalence. Localized climatic and socioeconomic factors are investigated as potential drivers of these observed epidemiological patterns. Conclusions Our results show that lower prevalence is associated with higher degrees of aggregation and hence prevalence alone is not a good indicator of transmission intensity. As a consequence, approaches to MDA and monitoring and evaluation of community infection status may need to be adapted as transmission elimination is aimed for by targeted treatment approaches.

scholarly journals Incubation periods impact the spatial predictability of outbreaks: analysis of cholera and Ebola outbreaks in Sierra Leone

2019 ◽  
Author(s):  
Rebecca Kahn ◽  
Corey M. Peak ◽  
Juan Fernández-Gracia ◽  
Alexandra Hill ◽  
Amara Jambai ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Sierra Leone ◽  
Incubation Period ◽  
Population Connectivity ◽  
Disease Spread ◽  
Long Distance ◽  
Spatial Spread ◽  
Transmission Parameters ◽  
Incubation Periods ◽  
The Impact

AbstractForecasting the spatiotemporal spread of infectious diseases during an outbreak is an important component of epidemic response. However, it remains challenging both methodologically and with respect to data requirements as disease spread is influenced by numerous factors, including the pathogen’s underlying transmission parameters and epidemiological dynamics, social networks and population connectivity, and environmental conditions. Here, using data from Sierra Leone we analyze the spatiotemporal dynamics of recent cholera and Ebola outbreaks and compare and contrast the spread of these two pathogens in the same population. We develop a simulation model of the spatial spread of an epidemic in order to examine the impact of a pathogen’s incubation period on the dynamics of spread and the predictability of outbreaks. We find that differences in the incubation period alone can determine the limits of predictability for diseases with different natural history, both empirically and in our simulations. Our results show that diseases with longer incubation periods, such as Ebola, where infected individuals can travel further before becoming infectious, result in more long-distance sparking events and less predictable disease trajectories, as compared to the more predictable wave-like spread of diseases with shorter incubation periods, such as cholera.Significance statementUnderstanding how infectious diseases spread is critical for preventing and containing outbreaks. While advances have been made in forecasting epidemics, much is still unknown. Here we show that the incubation period – the time between exposure to a pathogen and onset of symptoms – is an important factor in predicting spatiotemporal spread of disease and provides one explanation for the different trajectories of the recent Ebola and cholera outbreaks in Sierra Leone. We find that outbreaks of pathogens with longer incubation periods, such as Ebola, tend to have less predictable spread, whereas pathogens with shorter incubation periods, such as cholera, spread in a more predictable, wavelike pattern. These findings have implications for the scale and timing of reactive interventions, such as vaccination campaigns.

scholarly journals EVALUASI PERBANDINGAN PERFORMA JARINGAN WIRELESS SENSOR NETWORK DALAM SERANGAN SYBIL ATTACK DAN HELLO FLOOD ATTACK

Repositor ◽  
2020 ◽  
Vol 2 (8) ◽  
Author(s):  
Hawwin Purnama Akbar ◽  
Diah Risqiwati ◽  
Diah Risqiwati
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Ad Hoc ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Network Technology ◽  
Sybil Attack ◽  
Packet Delivery ◽  
The Impact ◽  
Hello Flood

Perkembangan ilmu pengetahuan pada bidang teknologi jaringan terjadi sangat cepat karena mengikuti perkembangan kebutuhan manusia. Salah satu teknologi jaringan yang saat ini menarik perhatian masyarakat adalah teknologi Wireless Sensor Network(WSN). WSN adalah jaringan dari kumpulan sensor yang terhubung menggunakan teknologi wireless secara ad-hoc dan setiap sensor node digunakan untuk proses pengumpulan data dan menghubungkan dengan node yang lain melalui jaringan wireless.Karena pada kebanyakan kasus aplikasi WSN digunakan pada lingkungan yang ekstrem dan sensor node harus dapat beroperasi secara otomatis tanpa campur tangan manusia, jaringan ini menjadi rentan akan beberapa ancaman jaringan dan dapat mempengaruhi performa dari jaringannya. Terdapat berbagai macam jenis serangan yang dapat membahayakan jaringan wireless sensor network diantaranya yang paling umum adalah sybil attack dan hello flood attack.            Dalam penelitian ini, penulis meneliti performa WSN saat diserang oleh Sybil attack dan hello flood attack dengan cara mengukur throughput, PDR(packet delivery ratio), jitter dan delay dalam jaringan WSN. Penelitian ini juga menganalisa jumlah node yang bervariasi dari 10 node sampai 30 node dengan waktu simulasi dari 10 detik sampai 30 detik lalu dianalisa jaringan ketika jaringan normal dan diserang oleh node penyerang yang bervariasi dari 1 sampai 3 penyerang. Dengan melakukan analisa tersebut, diperoleh data berupa perbandingan dampak serangan dari Sybil attack dan hello flood attack. Dampak dari sybil attack lebih berpengaruh pada parameter throughput dan pdr yang mengalami penurunan nilai hingga 69,9% untuk pdr dan 56,4% untuk throughput. Sedangkan dampak dari hello flood attack lebih berpengaruh pada parameter delay dan jitter yang mengalami kenaikandari nilai semula 0,05 detik menjadi 0,576 detikuntuk delay dan 0,579 detik untuk jitter.AbstractThe development of science in the field of network technology occurs very quickly because it follows the development of human needs. One of the network technology that is currently attracting public attention is wireless sensor network technology (WSN). WSN is a network of connected sensors using ad-hoc wireless technology and each node sensor are used to process data collection and connect with other nodes over a wireless network. Because in most cases WSN applications are used in extreme environments and node sensors must operate automatically without human intervention, these networks become vulnerable to some network threats and may affect the performance of their networks. There are various types of attacks that can harm wireless sensor network network among the most common is sybil attack and hello flood attack.             In this study, authors examined the performance of WSN when attacked by Sybil attack and hello flood attack by measuring throughput, PDR (packet delivery ratio), jitter and delay in WSN network. This study also analyzed the number of nodes that varied from 10 nodes to 30 nodes with simulated time from 10 seconds to 30 seconds and then analyzed the network when the network was normal and attacked by the attacking nodes that varied from 1 to 3 attackers. By doing the analysis, the datacan be obtained in the form of comparison of the impact of attacks from Sybil attack and hello flood attack. The impact of the sybil attack is more influential on the parameters of throughput and pdr which has decreased the maximum value up to 69.9% for pdr and 56.4% for throughput. While the impact of hello flood attack ismore influential on the delay and jitter parameters that increased from the original value of 0.05 seconds to 0.576 seconds for delay and 0.579 seconds for jitter. 

scholarly journals Using the infection fatality rate to predict the evolution of Covid-19 in Brazil

2020 ◽  
Author(s):  
M. S. Cecconello ◽  
G. L. Diniz ◽  
E. B. Silva
Keyword(s):  
Sir Model ◽  
Fatality Rate ◽  
Social Distancing ◽  
Governmental Agencies ◽  
Transmission Parameters ◽  
The Impact

AbstractIn this work we are going to use estimates of Infection Fatality Rate (IFR) for Covid-19 in order to predict the evolution of Covid-19 in Brazil. To this aim, we are going to fit the parameters of the SIR model using the official deceased data available by governmental agencies. Furthermore, we are going to analyse the impact of social distancing policies on the transmission parameters.

scholarly journals Analyzing the impact of the number of nodes on the performance of the routing protocols in manet environment

2021 ◽  
Vol 10 (1) ◽  
pp. 434-440
Author(s):  
Hussein M. Haglan ◽  
Salama A. Mostafa ◽  
Noor Zuraidin Mohd Safar ◽  
Aida Mustapha ◽  
Mohd. Zainuri Saringatb ◽  
...  
Keyword(s):  
Routing Protocols ◽  
Ad Hoc ◽  
Variable Number ◽  
Delivery Ratio ◽  
Packet Delivery ◽  
Communication Structures ◽  
End To End Delay ◽  
Mobile Ad Hoc ◽  
Hoc Networks ◽  
The Impact

Mobile Ad-hoc Networks (MANETs) are independent systems that can work without the requirement for unified controls, pre-setup to the paths/routes or advance communication structures. The nodes/hubs of a MANET are independently controlled, which permit them to behave unreservedly in a randomized way inside the MANET. The hubs can leave their MANET and join different MANETs whenever the need arises. These attributes, in any case, may contrarily influence the performance of the routing conventions (or protocols) and the general topology of the systems. Along these lines, MANETs include uniquely planned routing conventions that responsively as well as proactively carry out the routing. This paper assesses and looks at the effectiveness (or performance) of five directing conventions which are AOMDV, DSDV, AODV, DSR and OLSR in a MANET domain. The research incorporates executing a simulating environment to look at the operation of the routing conventions dependent on the variable number of hubs. Three evaluation indices are utilized: Throughput (TH), Packet Delivery Ratio (PDR), and End-to-End delay (E2E). The assessment outcomes indicate that the AODV beats other conventions in the majority of the simulated scenarios.

scholarly journals Modeling the Impact of Seasonal Weather Variations on the Infectiology of Brucellosis

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Nkuba Nyerere ◽  
Livingstone S. Luboobi ◽  
Saul C. Mpeshe ◽  
Gabriel M. Shirima
Keyword(s):  
Mathematical Model ◽  
Numerical Simulations ◽  
Transmission Dynamics ◽  
Wild Animals ◽  
Indirect Transmission ◽  
Transmission Parameters ◽  
Domestic Ruminants ◽  
Weather Variations ◽  
The Impact ◽  
Seasonal Weather

A deterministic mathematical model for brucellosis that incorporates seasonality on direct and indirect transmission parameters for domestic ruminants, wild animals, humans, and the environment was formulated and analyzed in this paper. Both analytical and numerical simulations are presented. From this study, the findings show that variations in seasonal weather have the great impact on the transmission dynamics of brucellosis in humans, livestock, and wild animals. Thus, in order for the disease to be controlled or eliminated, measures should be timely implemented upon the fluctuation in the transmission of the disease.

scholarly journals Probabilistic modelling of effects of antibiotics and calendar time on transmission of healthcare-associated infection

2020 ◽  
Author(s):  
Mirjam Laager ◽  
Ben S Cooper ◽  
David W Eyre
Keyword(s):  
Temporal Trends ◽  
Simulated Data ◽  
Healthcare Associated Infection ◽  
Calendar Time ◽  
Transmission Parameters ◽  
Number Of Patients ◽  
Potential Risk Factors ◽  
Healthcare Associated ◽  
The Impact ◽  
Over Time

AbstractHealthcare-associated infection and antimicrobial resistance are major concerns. However, the extent to which antibiotic exposure affects transmission and detection of infections such as MRSA is unclear. Additionally, temporal trends are typically reported in terms of changes in incidence, rather than analysing underling transmission processes. We present a data-augmented Markov chain Monte Carlo approach for inferring changing transmission parameters over time, screening test sensitivity, and the effect of antibiotics on detection and transmission. We expand a basic model to allow use of typing information when inferring sources of infections. Using simulated data, we show that the algorithms are accurate, well-calibrated and able to identify antibiotic effects in sufficiently large datasets. We apply the models to study MRSA transmission in an intensive care unit in Oxford, UK with 7924 admissions over 10 years. We find that falls in MRSA incidence over time were associated with decreases in both the number of patients admitted to the ICU colonised with MRSA and in transmission rates. In our inference model, the data were not informative about the effect of antibiotics on risk of transmission or acquisition of MRSA, a consequence of the limited number of possible transmission events in the data. Our approach has potential to be applied to a range of healthcare-associated infections and settings and could be applied to study the impact of other potential risk factors for transmission. Evidence generated could be used to direct infection control interventions.

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