scholarly journals Host size and proximity to diseased neighbours drive the spread of a coral disease outbreak in Hawai‘i

2018 ◽  
Vol 285 (1870) ◽  
pp. 20172265 ◽  
Author(s):  
Jamie M. Caldwell ◽  
Megan J. Donahue ◽  
C. Drew Harvell

Understanding how disease risk varies over time and across heterogeneous populations is critical for managing disease outbreaks, but this information is rarely known for wildlife diseases. Here, we demonstrate that variation in host and pathogen factors drive the direction, duration and intensity of a coral disease outbreak. We collected longitudinal health data for 200 coral colonies, and found that disease risk increased with host size and severity of diseased neighbours, and disease spread was highest among individuals between 5 and 20 m apart. Disease risk increased by 2% with every 10 cm increase in host size. Healthy colonies with severely diseased neighbours (greater than 75% affected tissue) were 1.6 times more likely to develop disease signs compared with colonies with moderately diseased neighbours (25–75% affected tissue). Force of infection ranged from 7 to 20 disease cases per 1000 colonies (mean = 15 cases per 1000 colonies). The effective reproductive ratio, or average number of secondary infections per infectious individual, ranged from 0.16 to 1.22. Probability of transmission depended strongly on proximity to diseased neighbours, which demonstrates that marine disease spread can be highly constrained within patch reefs.

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
G. S. Aeby ◽  
D. G. Bourne ◽  
B. Wilson ◽  
T. M. Work

The dynamics of the coral disease,Acroporawhite syndrome (AWS), was directly compared on reefs in the species-poor region of the Northwestern Hawaiian Islands (NWHI) and the species-rich region of American Samoa (AS) with results suggesting that biodiversity, which can affect the abundance of susceptible hosts, is important in influencing the impacts of coral disease outbreaks. The diversity-disease hypothesis predicts that decreased host species diversity should result in increased disease severity of specialist pathogens. We found that AWS was more prevalent and had a higher incidence within the NWHI as compared to AS. IndividualAcroporacolonies affected by AWS showed high mortality in both regions, but case fatality rate and disease severity was higher in the NWHI. The site within the NWHI had a monospecific stand ofA. cytherea; a species that is highly susceptible to AWS. Once AWS entered the site, it spread easily amongst the abundant susceptible hosts. The site within AS contained numerousAcroporaspecies, which differed in their apparent susceptibility to infection and disease severity, which in turn reduced disease spread. Manipulative studies showed AWS was transmissible through direct contact in threeAcroporaspecies. These results will help managers predict and respond to disease outbreaks.


2018 ◽  
Author(s):  
Fletcher W. Halliday ◽  
Jason R. Rohr

AbstractDiverse host communities commonly inhibit the spread of parasites in studies at small and intermediate scales, leading some to suggest that conserving biodiversity could help control infectious diseases. However, the generality of this “dilution effect” remains controversial. First, most studies assume a linear, monotonic relationship between biodiversity and disease, though the actual shape is unknown. Second, most studies are conducted at a single spatial scale, though biotic interactions are often-scale-dependent, thus spatial scale might determine the direction of biodiversity-disease relationships. Third, most studies focus only on a small range of possible diversity levels, though the direction of biodiversity-disease relationships may change outside of this range. By analyzing 231 biodiversity-disease relationships on 77 parasite species, we provide broad evidence that biodiversity-disease relationships are generally non-linear and moderated by spatial scale; biodiversity generally inhibits disease at local scales (<100 km2) and amplifies disease at regional scales (>1,000,000 km2). These effects did not depend on any tested host, parasite, or study characteristics, though the spatial scale of a study was often related to study design and parasite type, highlighting the need for additional multiscale research. Few studies were missing substantial data at low diversity, but missing data at low diversity could result in underreporting of amplification. Experiments might be missing data at high diversity, which could result in underreporting of dilution. Despite context-dependence in biodiversity-disease relationships, most conservation is implemented at local scales where biodiversity appears to inhibit disease and thus these results suggest that local conservation actions could reduce disease risk.Significance statementIt has been suggested that diverse ecological communities limit disease spread, but the generality of this pattern is contentious. Therefore, the degree to which biodiversity conservation can limit harmful epidemics remains unresolved. We address this fundamental question by analyzing 231 published relationships between biodiversity and disease. We find evidence that most biodiversity-disease relationships are nonlinear and scale-dependent with biodiversity generally associated with reduced disease at small and intermediate scales, but increased disease at large scales. Moreover, these results were generally robust to missing data at low and high biodiversity levels and variation in host, parasite, and study characteristics. This suggests that conservation efforts aimed at reducing the impacts of human and wildlife diseases will be most successful at local scales.


2016 ◽  
Author(s):  
Carrie A. Manore ◽  
Richard S. Ostfeld ◽  
Folashade B. Agusto ◽  
Holly Gaff ◽  
Shannon L. LaDeau

AbstractThe recent spread of mosquito-transmitted viruses and associated disease to the Americas motivates a new, data-driven evaluation of risk in temperate population centers. Temperate regions are generally expected to pose low risk for significant mosquito-borne disease, however, the spread of the Asian tiger mosquito (Aedes albopictus) across densely populated urban areas has established a new landscape of risk. We use a model informed by field data to assess the conditions likely to facilitate local transmission of chikungunya and Zika viruses from an infected traveler toAe. albopictusand then to other humans in USA cities with variable human densities and seasonality.Mosquito-borne disease occurs when specific combinations of conditions maximize virus-to-mosquito and mosquito-to-human contact rates. We develop a mathematical model that captures the epidemiology and is informed by current data on vector ecology from urban sites. The model predicts that one of every two infectious travelers arriving at peak mosquito season could initiate local transmission and > 10% of the introductions could generate a disease outbreak of at least 100 people. DespiteAe. albopictuspropensity for biting non-human vertebrates, we also demonstrate that local virus transmission and human outbreaks may occur when vectors feed from humans even just 40% of the time. This work demonstrates how a conditional series of non-average events can result in local arbovirus transmission and outbreaks of disease in humans, even in temperate cities.Author SummaryZika and chikungunya viruses are transmitted byAedesmosquitoes, includingAe. albopictus, which is abundant in many temperate cities. While disease risk is lower in temperate regions where viral amplification cannot build across years, there is significant potential for localized disease outbreaks in urban populations. We use a model informed by field data to assess the conditions likely to facilitate local transmission of virus from an infected traveler toAe. albopictusand then to other humans in USA cities with variable human densities and seasonality. The model predicts that one of every two infectious travelers arriving at peak mosquito season could initiate local transmission and > 10% of the introductions could generate a disease outbreak of >100 people.Classification: Ecology


2020 ◽  
Author(s):  
Andrew Fang ◽  
Jonathan Kia-Sheng Phua ◽  
Terrence Chiew ◽  
Daniel De-Liang Loh ◽  
Lincoln Ming Han Liow ◽  
...  

BACKGROUND During the Coronavirus Disease 2019 (COVID-19) outbreak, community care facilities (CCF) were set up as temporary out-of-hospital isolation facilities to contain the surge of cases in Singapore. Confined living spaces within CCFs posed an increased risk of communicable disease spread among residents. OBJECTIVE This inspired our healthcare team managing a CCF operation to design a low-cost communicable disease outbreak surveillance system (CDOSS). METHODS Our CDOSS was designed with the following considerations: (1) comprehensiveness, (2) efficiency through passive reconnoitering from electronic medical record (EMR) data, (3) ability to provide spatiotemporal insights, (4) low-cost and (5) ease of use. We used Python to develop a lightweight application – Python-based Communicable Disease Outbreak Surveillance System (PyDOSS) – that was able perform syndromic surveillance and fever monitoring. With minimal user actions, its data pipeline would generate daily control charts and geospatial heat maps of cases from raw EMR data and logged vital signs. PyDOSS was successfully implemented as part of our CCF workflow. We also simulated a gastroenteritis (GE) outbreak to test the effectiveness of the system. RESULTS PyDOSS was used throughout the entire duration of operation; the output was reviewed daily by senior management. No disease outbreaks were identified during our medical operation. In the simulated GE outbreak, PyDOSS was able to effectively detect an outbreak within 24 hours and provided information about cluster progression which could aid in contact tracing. The code for a stock version of PyDOSS has been made publicly available. CONCLUSIONS PyDOSS is an effective surveillance system which was successfully implemented in a real-life medical operation. With the system developed using open-source technology and the code made freely available, it significantly reduces the cost of developing and operating CDOSS and may be useful for similar temporary medical operations, or in resource-limited settings.


Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
M. V. Barbarossa ◽  
M. Polner ◽  
G. Röst

We investigate the temporal evolution of the distribution of immunities in a population, which is determined by various epidemiological, immunological, and demographical phenomena: after a disease outbreak, recovered individuals constitute a large immune population; however, their immunity is waning in the long term and they may become susceptible again. Meanwhile, their immunity can be boosted by repeated exposure to the pathogen, which is linked to the density of infected individuals present in the population. This prolongs the length of their immunity. We consider a mathematical model formulated as a coupled system of ordinary and partial differential equations that connects all these processes and systematically compare a number of boosting assumptions proposed in the literature, showing that different boosting mechanisms lead to very different stationary distributions of the immunity at the endemic steady state. In the situation of periodic disease outbreaks, the waveforms of immunity distributions are studied and visualized. Our results show that there is a possibility to infer the boosting mechanism from the population level immune dynamics.


PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255680
Author(s):  
William R. Milligan ◽  
Zachary L. Fuller ◽  
Ipsita Agarwal ◽  
Michael B. Eisen ◽  
Molly Przeworski ◽  
...  

New emerging infectious diseases are identified every year, a subset of which become global pandemics like COVID-19. In the case of COVID-19, many governments have responded to the ongoing pandemic by imposing social policies that restrict contacts outside of the home, resulting in a large fraction of the workforce either working from home or not working. To ensure essential services, however, a substantial number of workers are not subject to these limitations, and maintain many of their pre-intervention contacts. To explore how contacts among such “essential” workers, and between essential workers and the rest of the population, impact disease risk and the effectiveness of pandemic control, we evaluated several mathematical models of essential worker contacts within a standard epidemiology framework. The models were designed to correspond to key characteristics of cashiers, factory employees, and healthcare workers. We find in all three models that essential workers are at substantially elevated risk of infection compared to the rest of the population, as has been documented, and that increasing the numbers of essential workers necessitates the imposition of more stringent controls on contacts among the rest of the population to manage the pandemic. Importantly, however, different archetypes of essential workers differ in both their individual probability of infection and impact on the broader pandemic dynamics, highlighting the need to understand and target intervention for the specific risks faced by different groups of essential workers. These findings, especially in light of the massive human costs of the current COVID-19 pandemic, indicate that contingency plans for future epidemics should account for the impacts of essential workers on disease spread.


10.2196/21685 ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. e21685
Author(s):  
Zonglin He ◽  
Casper J P Zhang ◽  
Jian Huang ◽  
Jingyan Zhai ◽  
Shuang Zhou ◽  
...  

A novel pneumonia-like coronavirus disease (COVID-19) caused by a novel coronavirus named SARS-CoV-2 has swept across China and the world. Public health measures that were effective in previous infection outbreaks (eg, wearing a face mask, quarantining) were implemented in this outbreak. Available multidimensional social network data that take advantage of the recent rapid development of information and communication technologies allow for an exploration of disease spread and control via a modernized epidemiological approach. By using spatiotemporal data and real-time information, we can provide more accurate estimates of disease spread patterns related to human activities and enable more efficient responses to the outbreak. Two real cases during the COVID-19 outbreak demonstrated the application of emerging technologies and digital data in monitoring human movements related to disease spread. Although the ethical issues related to using digital epidemiology are still under debate, the cases reported in this article may enable the identification of more effective public health measures, as well as future applications of such digitally directed epidemiological approaches in controlling infectious disease outbreaks, which offer an alternative and modern outlook on addressing the long-standing challenges in population health.


Author(s):  
Ashley Heida ◽  
Alexis Mraz ◽  
Mark Hamilton ◽  
Mark Weir ◽  
Kerry A Hamilton

Legionella pneumophila are bacteria that when inhaled cause Legionnaires’ Disease (LD) and febrile illness Pontiac Fever. As of 2014, LD is the most frequent cause of waterborne disease outbreaks due...


2019 ◽  
Vol 147 ◽  
Author(s):  
S. J. Chai ◽  
W. Gu ◽  
K. A. O'Connor ◽  
L. C. Richardson ◽  
R. V. Tauxe

Abstract Early in a foodborne disease outbreak investigation, illness incubation periods can help focus case interviews, case definitions, clinical and environmental evaluations and predict an aetiology. Data describing incubation periods are limited. We examined foodborne disease outbreaks from laboratory-confirmed, single aetiology, enteric bacterial and viral pathogens reported to United States foodborne disease outbreak surveillance from 1998–2013. We grouped pathogens by clinical presentation and analysed the reported median incubation period among all illnesses from the implicated pathogen for each outbreak as the outbreak incubation period. Outbreaks from preformed bacterial toxins (Staphylococcus aureus, Bacillus cereus and Clostridium perfringens) had the shortest outbreak incubation periods (4–10 h medians), distinct from that of Vibrio parahaemolyticus (17 h median). Norovirus, salmonella and shigella had longer but similar outbreak incubation periods (32–45 h medians); campylobacter and Shiga toxin-producing Escherichia coli had the longest among bacteria (62–87 h medians); hepatitis A had the longest overall (672 h median). Our results can help guide diagnostic and investigative strategies early in an outbreak investigation to suggest or rule out specific etiologies or, when the pathogen is known, the likely timeframe for exposure. They also point to possible differences in pathogenesis among pathogens causing broadly similar syndromes.


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