scholarly journals Delays in Epidemic Outbreak Control Cost Disproportionately Large Treatment Footprints to Offset

2021 ◽  
Author(s):  
Paul M. Severns ◽  
Christopher C. Mundt
Keyword(s):  
Disease Surveillance ◽  
Disease Spread ◽  
Epidemic Outbreak ◽  
Treatment Area ◽  
Epidemic Disease ◽  
Long Distance ◽  
Treatment Timing ◽  
Outbreak Control ◽  
Outbreak Area ◽  
Surveillance Programs

Abstract Epidemic outbreak control often involves a spatially-explicit treatment area (quarantine, inoculation, ring cull) which covers the outbreak area and adjacent regions where hosts are thought to be latently infected. Emphasis on space however neglects the influence of treatment timing on outbreak control. We conducted field and in-silico experiments with wheat stripe rust (WSR), a long-distance dispersed plant disease, to understand interactions between treatment timing and area interact to suppress an outbreak. Full-factorial field experiments with three different ring culls (outbreak area only to a 25-fold increase in treatment area) at three different disease control timings (1.125, 1.25, and 1.5 latent periods after initial disease expression), indicated that earlier treatment timing had a conspicuously greater suppressive effect than the area treated. Disease spread computer simulations over a broad range of influential epidemic parameter values (R0, outbreak disease prevalence, epidemic duration) suggested that potentially unrealistically large increases in treatment area would be required to compensate for even small delays in treatment timing. Although disease surveillance programs are costly, our results suggest that treatments early in an epidemic disease outbreak require smaller areas to be effective, which may ultimately compensate for the upfront costs of proactive disease surveillance programs.

scholarly journals Variation in human mobility and its impact on the risk of future COVID-19 outbreaks in Taiwan

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Meng-Chun Chang ◽  
Rebecca Kahn ◽  
Yu-An Li ◽  
Cheng-Sheng Lee ◽  
Caroline O. Buckee ◽  
...  
Keyword(s):  
Disease Surveillance ◽  
Initial Conditions ◽  
Human Mobility ◽  
Human Movement ◽  
Disease Spread ◽  
Spatial Spread ◽  
Movement Data ◽  
Intercity Travel ◽  
Travel Restrictions ◽  
The Impact

Abstract Background As COVID-19 continues to spread around the world, understanding how patterns of human mobility and connectivity affect outbreak dynamics, especially before outbreaks establish locally, is critical for informing response efforts. In Taiwan, most cases to date were imported or linked to imported cases. Methods In collaboration with Facebook Data for Good, we characterized changes in movement patterns in Taiwan since February 2020, and built metapopulation models that incorporate human movement data to identify the high risk areas of disease spread and assess the potential effects of local travel restrictions in Taiwan. Results We found that mobility changed with the number of local cases in Taiwan in the past few months. For each city, we identified the most highly connected areas that may serve as sources of importation during an outbreak. We showed that the risk of an outbreak in Taiwan is enhanced if initial infections occur around holidays. Intracity travel reductions have a higher impact on the risk of an outbreak than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. The timing, duration, and level of travel reduction together determine the impact of travel reductions on the number of infections, and multiple combinations of these can result in similar impact. Conclusions To prepare for the potential spread within Taiwan, we utilized Facebook’s aggregated and anonymized movement and colocation data to identify cities with higher risk of infection and regional importation. We developed an interactive application that allows users to vary inputs and assumptions and shows the spatial spread of the disease and the impact of intercity and intracity travel reduction under different initial conditions. Our results can be used readily if local transmission occurs in Taiwan after relaxation of border control, providing important insights into future disease surveillance and policies for travel restrictions.

scholarly journals Sheep scab spatial distribution: the roles of transmission pathways

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Emily Joanne Nixon ◽  
Ellen Brooks-Pollock ◽  
Richard Wall
Keyword(s):  
Disease Transmission ◽  
Movement Control ◽  
Cost Effective ◽  
Infected Sheep ◽  
Disease Spread ◽  
Psoroptes Ovis ◽  
Long Distance ◽  
Sheep Scab ◽  
The Uk ◽  
The Impact

Abstract Background Ovine psoroptic mange (sheep scab) is a highly pathogenic contagious infection caused by the mite Psoroptes ovis. Following 21 years in which scab was eradicated in the UK, it was inadvertently reintroduced in 1972 and, despite the implementation of a range of control methods, its prevalence increased steadily thereafter. Recent reports of resistance to macrocyclic lactone treatments may further exacerbate control problems. A better understanding of the factors that facilitate its transmission are required to allow improved management of this disease. Transmission of infection occurs within and between contiguous sheep farms via infected sheep-to-sheep or sheep–environment contact and through long-distance movements of infected sheep, such as through markets. Methods A stochastic metapopulation model was used to investigate the impact of different transmission routes on the spatial pattern of outbreaks. A range of model scenarios were considered following the initial infection of a cluster of highly connected contiguous farms. Results Scab spreads between clusters of neighbouring contiguous farms after introduction but when long-distance movements are excluded, infection then self-limits spatially at boundaries where farm connectivity is low. Inclusion of long-distance movements is required to generate the national patterns of disease spread observed. Conclusions Preventing the movement of scab infested sheep through sales and markets is essential for any national management programme. If effective movement control can be implemented, regional control in geographic areas where farm densities are high would allow more focussed cost-effective scab management. Graphical Abstract

scholarly journals Epidemiology of Lyme Disease

1991 ◽  
Vol 2 (2) ◽  
pp. 58-60
Author(s):  
Dennis J White
Keyword(s):  
Public Health ◽  
Lyme Disease ◽  
Disease Surveillance ◽  
Vector Species ◽  
Potential Vector ◽  
Public Health Agencies ◽  
Laboratory Services ◽  
Health Agencies ◽  
Surveillance Programs ◽  
Risk Of Exposure

Investigation of the epidemiology of Lyme disease depends upon information generated from several sources. Human disease surveillance can be conducted by both passive and active means involving physicians, public health agencies and laboratories. Passive and active tick surveillance programs can document the extent of tick-borne activity, identify the geographic range of potential vector species, and determine the relative risk of exposure to Lyme disease in specific areas. Standardized laboratory services can play an important role in providing data. Epidemiologists can gain a better understanding of Lyme disease through the collection of data from such programs. The interpretation of data and provision of information to the medical and general communities are important functions of public health agencies.

Tracking Epidemics

War Epidemics ◽  
2004 ◽  
Author(s):  
Matthew Smallman-Raynor ◽  
Andrew Cliff
Keyword(s):  
Inductive Reasoning ◽  
Added Value ◽  
Disease Spread ◽  
Epidemic Disease ◽  
Time Dynamics ◽  
Source Of Infection ◽  
German Army ◽  
Military Campaigns ◽  
Social Demographic ◽  
Medical Interest

In studies of past, present, and likely future disease distributions, the ‘added value’ provided by the geographer lies in three main areas: detecting spatial concentrations of disease; isolating the processes (environmental, social, demographic, and pathogenic) which cause these disease hotspots; and in enhancing our understanding of the space–time dynamics of disease spread. This is as true of war-related epidemics as of any others. Within geography, there is a long-standing tradition of mapping disease. In this early history, the incidence maps of yellow fever produced in 1798 are often given pride of place (Robinson, 1982). These were, however, pre-dated by maps of topics as diverse as hospital capacities and the distribution of dressing-stations on a battlefield, through to maps of pestilential swamps and other hostile medical environments. But, so far as most epidemiological reports were concerned, such maps were usually incidental. The breakthrough in disease mapping occurred in the middle of the nineteenth century with the cholera map produced by Dr John Snow to accompany the second edition of his prize-winning essay On the Mode of Communication of Cholera (1855a). What set Snow’s work apart was not the cartography (dot maps, which were a well-established cartographic device, to show the geographical distribution of individual cholera deaths), but his inductive reasoning from the map. By showing what he termed the ‘topography of the outbreak’, Snow was able to draw inferences about the central source of infection. The use of mapping as an important device for suggesting hypotheses of medical interest may be traced through to the present day. For war and disease, the classic example is the Seuchen Atlas. This atlas of epidemic disease (Zeiss, 1942–5; Anderson, 1947) was conceived by the German army as an adjunct to war, enhancing its ability to mount military campaigns. The atlas was produced as separate sheets over the years 1942–5. Its distribution was confined to military institutes and to those German university institutes involved in training medical students. The scope of the atlas was not global but confined largely to those areas where the Army High Command expected to be fighting.

Climate Change-Associated Conflict and Infectious Disease

2017 ◽  
pp. 68-88
Author(s):  
Devin C. Bowles
Keyword(s):  
Public Health ◽  
Climate Change ◽  
Infectious Disease ◽  
Infectious Diseases ◽  
Disease Surveillance ◽  
Health Personnel ◽  
Forced Migration ◽  
Scarce Resources ◽  
Food Scarcity ◽  
Surveillance Programs

One of the least appreciated mechanisms by which climate change will affect infectious diseases is via increased violent conflict. Climate change will diminish agricultural and pastoral resources and increase food scarcity in many areas, including already impoverished equatorial regions. Many in the defence and public health fields anticipate that climate change will increase conflict by fuelling competition over scarce resources. Already, some commentators argue that the conflicts in Darfur and Syria were partially caused or exacerbated by climate change. Conflict facilitates a range of conditions conducive to the spread of many infectious diseases, including malnutrition, forced migration, unhygienic living conditions and widespread sexual assault. Flight or killing of health personnel inhibits vaccination, vector control and disease surveillance programs. Emergence of new diseases may go undetected and discovery of outbreaks could be suppressed for strategic reasons. These conditions combine to increase the risk of pandemics.

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 The Relationship between the Migrant Population’s Migration Network and the Risk of COVID-19 Transmission in China—Empirical Analysis and Prediction in Prefecture-Level Cities

2020 ◽  
Vol 17 (8) ◽  
pp. 2630 ◽  
Author(s):  
Chenjing Fan ◽  
Tianmin Cai ◽  
Zhenyu Gai ◽  
Yuerong Wu
Keyword(s):  
Disease Transmission ◽  
Risk Level ◽  
Prevention Measures ◽  
Epidemic Outbreak ◽  
Long Distance ◽  
Migrant Populations ◽  
The Matrix ◽  
The Impact ◽  
The Relationship ◽  
Migration Network

The outbreak of COVID-19 in China has attracted wide attention from all over the world. The impact of COVID-19 has been significant, raising concerns regarding public health risks in China and worldwide. Migration may be the primary reason for the long-distance transmission of the disease. In this study, the following analyses were performed. (1) Using the data from the China migrant population survey in 2017 (Sample size = 432,907), a matrix of the residence–birthplace (R-B matrix) of migrant populations is constructed. The matrix was used to analyze the confirmed cases of COVID-19 at Prefecture-level Cities from February 1–15, 2020 after the outbreak in Wuhan, by calculating the probability of influx or outflow migration. We obtain a satisfactory regression analysis result (R2 = 0.826–0.887, N = 330). (2) We use this R-B matrix to simulate an outbreak scenario in 22 immigrant cities in China, and propose risk prevention measures after the outbreak. If similar scenarios occur in the cities of Wenzhou, Guangzhou, Dongguan, or Shenzhen, the disease transmission will be wider. (3) We also use a matrix to determine that cities in Henan province, Anhui province, and Municipalities (such as Beijing, Shanghai, Guangzhou, Shenzhen, Chongqing) in China will have a high risk level of disease carriers after a similar emerging epidemic outbreak scenario due to a high influx or outflow of migrant populations.

Implementation of Power Law Network Models of Epidemic Surveillance Data for Better Evaluation of Outbreak Detection Alarms

2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Razvan G. Romanescu ◽  
Rob Deardon
Keyword(s):  
Power Law ◽  
Disease Surveillance ◽  
Incidence Rates ◽  
Rate Of Change ◽  
Outbreak Detection ◽  
Disease Spread ◽  
Infectious Period ◽  
Contact Network ◽  
Human Populations ◽  
Susceptible Population

Abstract Properties of statistical alarms have been well studied for simple disease surveillance models, such as normally distributed incidence rates with a sudden or gradual shift in mean at the start of an outbreak. It is known, however, that outbreak dynamics in human populations depend significantly on the heterogeneity of the underlying contact network. The rate of change in incidence for a disease such as influenza peaks early on during the outbreak, when the most highly connected individuals get infected, and declines as the average number of connections in the remaining susceptible population drops. Alarm systems currently in use for detecting the start of influenza seasons generally ignore this mechanism of disease spread, and, as a result, will miss out on some early warning signals. We investigate the performance of various alarms on epidemics simulated from an undirected network model with a power law degree distribution for a pathogen with a relatively short infectious period. We propose simple custom alarms for the disease system considered, and show that they can detect a change in the process sooner than some traditional alarms. Finally, we test our methods on observed rates of influenza-like illness from two sentinel providers (one French, one Spanish) to illustrate their use in the early detection of the flu season.

Assessment of the ability of Aedes species mosquitoes to transmit feline Mycoplasma haemofelis and ‘Candidatus Mycoplasma haemominutum’

2016 ◽  
Vol 19 (8) ◽  
pp. 798-802 ◽  
Author(s):  
Krystle L Reagan ◽  
Lorelei L Clarke ◽  
Jennifer R Hawley ◽  
Phillip Lin ◽  
Michael R Lappin
Keyword(s):  
Blood Meal ◽  
Disease Surveillance ◽  
Health Department ◽  
Mycoplasma Species ◽  
Public Health Department ◽  
Feral Cat ◽  
Hemotropic Mycoplasma ◽  
Mycoplasma Wenyonii ◽  
Surveillance Programs ◽  
Mosquito Pool

Objectives The objective of this study was to evaluate wild-caught mosquitoes for evidence of hemotropic Mycoplasma species DNA and to determine whether the feline hemoplasmas, Mycoplasma haemofelis (Mhf) and ‘ Candidatus Mycoplasma haemominutum’ (Mhm), can be transmitted by Aedes aegypti mosquitoes in a laboratory setting. Methods Wild-caught mosquito pools (50 mosquitoes per pool, 84 pools) utilized in routine public health department disease surveillance programs were tested for hemotropic Mycoplasma species DNA using PCR with primers designed to amplify all known hemoplasmas. Additionally, mosquitoes were trapped in the vicinity of known feral cat colonies, pooled (50 mosquitoes per pool) and tested (84 pools). Purpose-bred cats housed in a research facility were infected with Mhf or Mhm and then colonized laboratory A aegypti were fed upon the bacteremic cats. After a 7 day incubation period, mosquitoes previously fed on infected cats were allowed to feed again on naive cats, which were monitored for bacteremia for 10 weeks. Results Mycoplasma wenyonii DNA was confirmed in one wild-caught mosquito pool by DNA sequencing. While 7% of cats tested in feral colonies were hemoplasma positive, none of the mosquitoes trapped near colonies were positive. Hemoplasma DNA was amplified from A aegypti by PCR immediately after the infectious blood meal, but DNA was not detected at 7 and 14 days after feeding. Although evidence for uptake of organisms existed, hemoplasma DNA was not amplified from the experimentally infested cats in the 10 week observation period. Conclusions and relevance While wild-caught mosquitoes contained hemoplasma DNA and laboratory reared A aegypti mosquitoes take up hemoplasmas during the blood meal, there was no evidence of biologic transmission in this model.

scholarly journals A new algorithm quantifies the roles of wind and midge flight activity in the bluetongue epizootic in northwest Europe

2012 ◽  
Vol 279 (1737) ◽  
pp. 2354-2362 ◽  
Author(s):  
Luigi Sedda ◽  
Heidi E. Brown ◽  
Bethan V. Purse ◽  
Laura Burgin ◽  
John Gloster ◽  
...  
Keyword(s):  
Flight Speed ◽  
Disease Spread ◽  
Model Framework ◽  
Long Distance ◽  
Vector Borne Diseases ◽  
Wind Speeds ◽  
Northwest Europe ◽  
Infected Insect ◽  
Vector Borne

The 2006 bluetongue (BT) outbreak in northwestern Europe had devastating effects on cattle and sheep in that intensively farmed area. The role of wind in disease spread, through its effect on Culicoides dispersal, is still uncertain, and remains unquantified. We examine here the relationship between farm-level infection dates and wind speed and direction within the framework of a novel model involving both mechanistic and stochastic steps. We consider wind as both a carrier of host semio-chemicals, to which midges might respond by upwind flight, and as a transporter of the midges themselves, in a more or less downwind direction. For completeness, we also consider midge movement independent of wind and various combinations of upwind, downwind and random movements. Using stochastic simulation, we are able to explain infection onset at 94 per cent of the 2025 affected farms. We conclude that 54 per cent of outbreaks occurred through (presumably midge) movement of infections over distances of no more than 5 km, 92 per cent over distances of no more than 31 km and only 2 per cent over any greater distances. The modal value for all infections combined is less than 1 km. Our analysis suggests that previous claims for a higher frequency of long-distance infections are unfounded. We suggest that many apparent long-distance infections resulted from sequences of shorter-range infections; a ‘stepping stone’ effect. Our analysis also found that downwind movement (the only sort so far considered in explanations of BT epidemics) is responsible for only 39 per cent of all infections, and highlights the effective contribution to disease spread of upwind midge movement, which accounted for 38 per cent of all infections. The importance of midge flight speed is also investigated. Within the same model framework, lower midge active flight speed (of 0.13 rather than 0.5 m s −1 ) reduced virtually to zero the role of upwind movement, mainly because modelled wind speeds in the area concerned were usually greater than such flight speed. Our analysis, therefore, highlights the need to improve our knowledge of midge flight speed in field situations, which is still very poorly understood. Finally, the model returned an intrinsic incubation period of 8 days, in accordance with the values reported in the literature. We argue that better understanding of the movement of infected insect vectors is an important ingredient in the management of future outbreaks of BT in Europe, and other devastating vector-borne diseases elsewhere.

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