Disease Warning System Research Articles

Hantavirus Pulmonary Syndrome Outbreak Anticipation by a Rapid Synchronous Increase in Rodent Abundance in the Northwestern Argentina Endemic Region: Towards an Early Warning System for Disease Based on Climate and Rodent Surveillance Data.

Hantavirus pulmonary syndrome (HPS) is an American emerging disease caused by the rodent-borne virus genus Orthohantavirus (Family: Hantaviridae: Order: Elliovirales Class: Bunyaviricetes). In Argentina, almost half of the HPS infections occur in the northwestern endemic region. In this study, we monitored rodent abundance during 2022 and 2023 in three sites with different sampling methods (removal trapping, live trapping and hunted rodents by domestic cats) to evaluate their relationship with human infections. We found a similar pattern of variation in rodent abundance across time, and particularly a synchronous rise of rodent abundance that anticipated an HPS outbreak in 2023. Our dynamic regression models revealed a positive relationship between HPS cases and rodent abundance with a three-month lag, as well as rainfall with an eight-month lag. Our results provide a framework for the planning and implementation of public health prevention campaigns based on climatology and rodent monitoring. Domestic cats bringing rodents into houses can be an overlooked risk factor, particularly if viral shedding of infected rodents is magnified by stress. HPS is a disease of public health concern due to its high mortality rate, the lack of a specific therapeutic treatment and no vaccine. Thus, prevention of infections is of the utmost importance.

Geostatistical Analysis of Apricot Shot Hole Disease and Influence Factors in District Nagar, Gilgit-Baltistan, Pakistan

Apricot (Prunus armeniaca L.) is the most common, high-income generative fruit and consequently plays an important part in the region's socio-economic growth. However, apricots are susceptible to several diseases and pests that cause lower yields and significant financial losses, including apricot shot hole disease brought on by Wilsonomyces carpophilus, a polyphagous fungus, creates a serious threat to stone fruits, particularly the apricot tree. The current study used descriptive statistics and geostatistical approaches to evaluate the geographical distribution of the apricot shot hole disease (5 valleys, 30 orchards, and 480 apricot trees) in district Nagar, Gilgit-Baltistan, Pakistan. From each apricot tree, 150 apricot fruits, a total of 72,000, were randomly selected to assess the incidence, disease index, and severity. Results revealed that the disease is spatially distributed within the study area, varying within valleys and orchards. The range of disease prevalence was 50.0-67.70%, incidence 56.97-64.01%, index 32.49-40.56% and mean severity 0.21-0.24%. Geostatistical techniques were used to predict the spatial dependency class. Results revealed that disease prevalence had a nugget/sill ratio of 0.915, inferring weak spatial dependence, whereas incidence, disease index, and severity inferred moderate spatial dependence with nugget/sill ratios of 0.479, 0.628, and 0.393, respectively. Moreover, the spherical semivariogram model, trend analysis graph, and GIS maps indicate the region's spatial distribution of apricot shot hole disease. This information linked to metrological data (temperature, precipitation, and relative humidity) seemingly favored the apricot shot hole disease development during the growing period. Evaluation of geostatistical mapping strategies in monitoring spatial distributions of apricot shot hole disease in a field setting will improve the decision support for disease management, selection of resistant variety, and improve sanitation condition of apricot orchards. Continuous climate and pest monitoring are essential for creating an efficient disease warning system for farmers and other organizations involved in agriculture to prevent future plant epidemics.

A pilot study on the advancement of livestock healthcare bio-capsules and development of customized long-range network in the United Arab Emirates

The UAE's environment is characterized by low rainfall, high temperatures, poor soil and lack of natural waterways. The country has been making tremendous efforts to build a sustainable agricultural and animal wealth sector that is better able to contribute to its national agenda of food security. Commercial cattle farming in such challenging conditions would require an effective disease prediction and warning system for optimizing the production dynamics. Therefore, we are proposing the use of smart livestock system comprising of orally ingested bio-capsules for monitoring the livestock health, connected through a customized long-range network (430 MHz) in the United Arab Emirates, which monitors the real time animal body condition data (temperature and body activity) using a mobile or web-based application. The study demonstrated a 100% success rate in estrus alarms in the test animals, 61.29% for calving alerts and an accurate disease prediction warning in all the animals diagnosed unhealthy. the conception rate in these estrus diagnosed animals with artificial insemination was 61.53%, which was significantly higher than the conception rate in the absence of this detection technology at the farm. The capsule was well tolerated in all the test animals and did not show any acute adverse or allergic reactions. This is the first of its kind of study using these advanced bio-capsules and development of the customized long-range network in the United Arab Emirates for smart livestock healthcare management.

Bacterial spot caused by Xanthomonas vesicatoria devastated tomato production of Khyber Pakhtunkhwa-Pakistan

In Khyber Pakhtunkhwa (KP) province-Pakistan, bacterial spot caused by Xanthomonas campestris pv. vesicatoria (Xcv) has decimated commercial tomato production. Field surveys were conducted in 2017 and 2018 to analyse the effects of the bacterial spot in tomato producing areas of KP, including Swat, Lower Dir, and Mansehra. During the growing seasons, diseased samples were collected from tomato fields, and disease incidence and severity were documented. The pathogen was isolated, purified, and morphologically and biochemically characterised. Most of the morphological and biochemical characteristics such as yellow colonies, were like those of Xanthomonas spp. Further, the pathogen was confirmed using XV1F/XV1R pair of species-specific primers, which amplify the atpD gene sequence solely in Xanthomonas vesicatoria. Out of 30 isolates, 16 isolates were confirmed to be Xcv. In these agro-ecological zones, this was the first report on the presence of Xcv. Furthermore, humidity and temperature were found to impact the incidence and severity of bacterial spot substantially. Most isolates grew at high temperatures and low humidity; however, some prefered high-temperature and low-humidity environments. In conclusion, bacterial spot disease caused by Xcv has now spread throughout all tomato-producing areas of KP province. To avert future plant epidemics, ongoing climate and pest monitoring is required to build an effective disease warning system for producers and other agriculture-related agencies.

Application and reflection of laboratory-based monitoring in early warning of infectious diseases

Despite the fact that our cognition towards infectious disease prevention, the advanced technology and the economic status of the whole society has made a great progress in the last decade, the outbreak of COVID-19 pneumonia has again enabled the public to acquire more about super-challenges of infectious diseases, epidemics and the relevant preventive measurements. In order to identify the epidemic signals in early stage or even before the onset of epidemic, the data research and utilization of a series of factors related to the occurrence and transmission of infectious diseases have played a significant role in research of prevention and control during the whole period of surveillance and early warning. Laboratory-based monitoring for the etiology has always been an important part of infectious disease warning system due to pathogens as the direct cause of such diseases. China has initially established a laboratory-based monitoring and early warning system for bacterial infectious diseases based on the Chinese Pathogen Identification Network with an aim to identify pathogens, outbreaks and sources. This network has played an essential role in early detection, tracking and precise prevention and control of bacterial infectious diseases, such as plague, cholera, and epidemic cerebrospinal meningitis. This issue focuses on the function of laboratory-based monitoring during the period of early warning, prevention, and control of bacterial infectious diseases, and conducted a wide range of researches based on the analysis of the epidemic and outbreak isolates, together with field epidemiological studies and normal monitoring systems. All of these could illustrate the effect of laboratory surveillance in the infectious disease risk assessment and epidemic investigation. At the same time, we have put forward our review and expectation of scenarios about laboratory-based monitoring and early warning technologies to provide innovative thoughts for promoting a leapfrog development of infectious disease monitoring and early warning system in China.

Modeling risk of Sclerotinia sclerotiorum-induced disease development on canola and dry bean using machine learning algorithms

Diseases caused by the fungus Sclerotinia sclerotiorum are managed mainly through fungicide applications in canola and dry bean. Accurate estimation of the risk of disease development on these crops could help farmers make spraying decisions. Five machine learning (ML) models were evaluated in classification and regression modes for predicting disease establishment under different air temperatures and leaf wetness duration conditions. Model algorithms were trained and tested using 20-fold cross validation. Correspondence between predicted and observed values were measured using Cohen’s Kappa (classification) and Lin’s concordance coefficients (regression). The artificial neural network (ANN) algorithms had average accuracies ≥ 89% (classification) and R2 ≥ 88% (regression) on canola and dry bean and their correspondence agreements were ≥ 0.83, which is considered substantial to almost perfect. In contrast, logistic regression algorithms had accuracies of 88% for dry bean and 78% for canola; other models were similarly inconsistent. Implementation of ANN models in disease warning systems could help farmers with spraying decisions. At the same time, these models provide insights on temperature and leaf wetness requirements for development of S. sclerotiorum diseases in these crops. Results of this study show the potential of ML models as tools for epidemiological studies on other pathosystems.

Evaluation of Early Warning, Alert and Response System for Ebola Virus Disease, Democratic Republic of the Congo, 2018-2020.

The 10th and largest Ebola virus disease epidemic in the Democratic Republic of the Congo (DRC) was declared in North Kivu Province in August 2018 and ended in June 2020. We describe and evaluate an Early Warning, Alert and Response System (EWARS) implemented in the Beni health zone of DRC during August 5, 2018–June 30, 2020. During this period, 194,768 alerts were received, of which 30,728 (15.8%) were validated as suspected cases. From these, 801 confirmed and 3 probable cases were detected. EWARS showed an overall good performance: sensitivity and specificity >80%, nearly all (97%) of alerts investigated within 2 hours of notification, and good demographic representativeness. The average cost of the system was US $438/case detected and US $1.8/alert received. The system was stable, despite occasional disruptions caused by political insecurity. Our results demonstrate that EWARS was a cost-effective component of the Ebola surveillance strategy in this setting.

Predictive Modeling of Brown Rust of Sugarcane Based on Temperature and Relative Humidity in Florida.

Logistic regression models were developed from 5 years (2014 to 2018) of disease severity and weather data in an attempt to predict brown rust of sugarcane at the Everglades Research and Education Center in Belle Glade, Florida. Disease severity (percentage area of the top visible dewlap leaf covered by rust) was visually assessed in the field every 2 weeks for two varieties susceptible to brown rust. A total of 250 variables were derived from weather data for 10- to 40-day periods before each brown rust assessment day. A subset of these variables were then evaluated as potential predictors of severity of brown rust based on their individual correlation or their biological meaningfulness. Analyses of correlation and stepwise logistic regression allowed us to identify afternoon humid thermal ratio (AHTR), temperature-based duration variables, and their interaction terms as the most significant variables associated with brown rust epidemics of sugarcane in Florida. The nine best predictive models were identified based on model accuracy, sensitivity, specificity, and estimates of the prediction error. The prediction accuracy of these models ranged from 73 to 85%. Single-variable model BR2 (based on AHTR) classified 89% of the epidemic and 81% of the nonepidemic status of the disease. More than 83% of the epidemics and 81% of the nonepidemic status of sugarcane brown rust was correctly classified via multiple-variable models. These models can be used as components of a rust disease warning system to assist in the management of brown rust epidemics of sugarcane in south Florida.

Machine learning algorithms for forecasting the incidence of Coffea arabica pests and diseases.

Disease and pest alert models are able to generate information for agrochemical applications only when needed, reducing costs and environmental impacts. With machine learning algorithms, it is possible to develop models to be used in disease and pest warning systems as a function of the weather in order to improve the efficiency of chemical control of pests of the coffee tree. Thus, we correlated the infection rates with the weather variables and also calibrated and tested machine learning algorithms to predict the incidence of coffee rust, cercospora, coffee miner, and coffee borer. We used weather and field data obtained from coffee plantations in production in the southern regions of the State of Minas Gerais (SOMG) and from the region of the Cerrado Mineiro; these crops did not receive phytosanitary treatments. The algorithms calibrated and tested for prediction were (a) Multiple linear regression (RLM); (b) K Neighbors Regressor (KNN); (c) Random Forest Regressor (RFT), and (d) Artificial Neural Networks (MLP). As dependent variables, we considered the monthly rates of coffee rust, cercospora, coffee miner, and coffee tree borer, and the weather elements were considered as independent (predictor) variables. Pearson correlation analyses were performed considering three different time periods, 1-10d (from 1 to 10days before the incidence evaluation), 11-20d, and 21-30d, and used to evaluate the unit correlations between the weather variables and infection rates of coffee diseases and pests. The models were calibrated in years of high and low yields, because the biannual variation of harvest yield of coffee beans influences the severity of the diseases. The models were compared by the Willmott's 'd', RMSE (root mean square error), and coefficient of determination (R2) indices. The result of the more accurate algorithm was specialized for the SOMG and Cerrado Mineiro regions using the kriging method. The weather variables that showed significant correlations with coffee rust disease were maximum air temperature, number of days with relative humidity above 80%, and relative humidity. RFT was more accurate in the prediction of coffee rust, cercospora, coffee miner, and coffee borer using weather conditions. In the SOMG, RFT showed a greater accuracy in the predictions for the Cerrado Mineiro in years of high and low yields and for all diseases. In SOMG, the RMSE values ranged from 0.227 to 0.853 for high-yield and 0.147 and 0.827 for low-yield coffee in the coffee borer forecasting.

Integrating cultivar resistance and disease warning system to control downy mildew of onion

The management of downy mildew of onion requires frequent applications of protective and systemic fungicides. In order to reduce production costs, we evaluated a control strategy that integrated the use of resistant experimental cultivar and a disease warning system. Regardless of the type of management adopted, the experimental cultivar was always more productive. In turn, management based on the applications of protective fungicide was more effective. Considering only the value of the fungicide and using the resistant experimental cultivar, we had a cost of BRL 3.5 cents/kg for management only with Antracol®; BRL 10.7 cents/kg for management based on the warning system; and BRL 14.8 cents/kg for management only with Ridomil® Gold MZ. The integration of different control strategies for downy mildew can bring greater productivity and profitability to farmers.

Soybean Rust Epidemics as Affected by Weather Conditions in Brazil

The soybean rust (SBR) epidemics are often triggered by weather conditions, which interfere actively on the disease progress. Therefore, weather variables can be used to estimate the risk of occurrence and severity of SBR outbreaks. This research aimed to determine the influence of weather variables on SBR progress in different field trials in Brazil. Field experiments were conducted during 2014-15 and 2015-16 soybean growing seasons in Piracicaba (SP), Ponta Grossa (PR), Campo Verde (MT) and Pedra Preta (MT). For all sites and seasons, a susceptible soybean cultivar was drilled with 0.45 m row spacing and 12 plants per linear meter. No fungicide sprays were applied to ensure natural disease occurrence. In order to create different environmental conditions, sequential sowing dates, of roughly 30-day intervals were carried out. In Piracicaba, Ponta Grossa, Campo Verde, and Pedra Preta the main weather variables influencing SBR were leaf wetness duration - LWD (R = 0.340), air temperature during LWD (R = 0.313), and cumulative rainfall (R = 0.304). The final severity was assessed only at Piracicaba and Ponta Grossa, and it was mainly influenced by LWD (R = 0.643). It is possible to conclude that epidemics of SBR were mainly influenced by leaf wetness duration, accumulated rainfall and air temperature during the LWD. Therefore, future researches aiming to develop a disease warning system for SBR should include the cumulative rainfall, LWD and the air temperature during LWD, together or individually, as inputs.

Examination and characterization of key factors to seasonal epidemics of downy mildew in native grape species Vitis davidii in southern China with the use of disease warning systems

Spine grape (Vitis davidii) is a major native grape species cultivated in south central China, and the industry is threatened by downy mildew outbreaks caused by Plasmopara viticola in this rainy and warm region. To examine and characterize the local epidemics of this disease in this region, experiments were conducted in Jingzhou in 2012–2013 with variety ‘Gaoshan-2’ and Zhongfang in 2013–2016 with variety ‘Xiangzhenzhu’ in Hunan Province. The number of diseased leaves with new infections (DL_Sum) and disease severity were assessed every 5 days. First, we used a mechanistic simulation model originally developed in Europe to simulate the primary inoculum seasons in these years and found that they ended in early May, suggesting little influences from oospores to the disease in summer. Then we used negative binomial regression to model the temporal changes of DL_Sum in summer. The predictive variables included weather variables and two disease risk indices derived based on two other empirical disease warning systems developed in Europe and North America. Relative humidity (RH) was identified the best predictive variable among all variables. Ordinal logistic regression identified mean temperature and days of heavy rainfall (>7 mm) as key weather factors for disease development. In general, RH was more important for infection process, and temperatures and rainfall were more important for symptom development and spore dispersal. Canopy structures could also have significant effects on disease development. These results may help for the development of new disease warning or forecast systems more suitable for this disease in southern China.

Validation of a Florida Strawberry Anthracnose Fruit Rot (AFR) Warning System in Iowa.

Field validation of a disease-warning system for strawberry anthracnose fruit rot (AFR), caused by Colletotrichum acutatum sensu lato, that was originally developed for use in Florida was conducted in Iowa from 2012 to 2014. Day-neutral strawberry (cv. Tristar) was artificially inoculated with the pathogen at the start of the fruit maturation period. A factorial combination in replicated trials of two spray timing methods (the Florida warning system and prescheduled, calendar-based timing) and two fungicides (captan and pyraclostrobin) was compared, along with a nonsprayed control. The calendar-based and warning system-based treatments provided statistically equivalent control of AFR incidence compared with the nonsprayed control, and the warning system treatments required an average of 1.7 fewer fungicide sprays annually than the calendar-based treatments. Further analysis of the field data suggested that the warning system might underestimate AFR risk under high disease pressure; in these circumstances, a lower action threshold value may need to be adopted. Overall, these results indicated that the Florida warning system can be valuable for helping Midwest strawberry growers control AFR with less reliance on fungicide sprays, but it may require modification to account for periods of high inoculum pressure, subject to results of further field trials.

Data Analysis and Forecasting of Tuberculosis Prevalence Rates for Smart Healthcare Based on a Novel Combination Model

In recent years, healthcare has attracted much attention, which is looking for more and more data analytics in healthcare to relieve medical problems in medical staff shortage, ageing population, people living alone, and quality of life. Data mining, analysis, and forecasting play a vital role in modern social and medical fields. However, how to select a proper model to mine and analyze the relevant medical information in the data is not only an extremely challenging problem, but also a concerning problem. Tuberculosis remains a major global health problem despite recent and continued progress in prevention and treatment. There is no doubt that the effective analysis and accurate forecasting of global tuberculosis prevalence rates lay a solid foundation for the construction of an epidemic disease warning and monitoring system from a global perspective. In this paper, the tuberculosis prevalence rate time series for four World Bank income groups are targeted. Kruskal–Wallis analysis of variance and multiple comparison tests are conducted to determine whether the differences of tuberculosis prevalence rates for different income groups are statistically significant or not, and a novel combined forecasting model with its weights optimized by a recently developed artificial intelligence algorithm—cuckoo search—is proposed to forecast the hierarchical tuberculosis prevalence rates from 2013 to 2016. Numerical results show that the developed combination model is not only simple, but is also able to satisfactorily approximate the actual tuberculosis prevalence rate, and can be an effective tool in mining and analyzing big data in the medical field.

Weather-based coffee leaf rust apparent infection rate modeling.

Brazil is the major coffee producer in the world, with 2 million hectares cropped, with 75% of this area with Coffea arabica and 25% with Coffea canephora. Coffee leaf rust (CLR) is one of the main diseases that cause yield losses by reducing healthy leaf area. As CLR is highly influenced by weather conditions, this study aimed to determine the best linearization model to estimate the CLR apparent infection rate, to correlate CLR infection rates with weather variables, and to develop and assess the performance of weather-based infection rate models to be used as a disease warning system. The CLR epidemic was analyzed for 88 site-seasons, while progress curves were assessed by linear, monomolecular, logistic, Gompertz, and exponential linearization models for apparent infection rate determination. Correlations between CLR infection rates and weather variables were conducted at different periods. From these correlations, multiple linear regressions were developed to estimate CLR infection rates, using the most weather-correlated variables. The Gompertz growth model had the best fit with CLR progress curves. Minimum temperature and relative humidity were the weather variables most correlated to infection rate and, therefore, chosen to compose a CLR forecast system. Among the models developed, the one for the condition of high coffee yield at a narrow row spacing was the best, with only 9.4% of false negative occurrences for all the months assessed.

Development and validation of a weather-based warning system to advise fungicide applications to control dollar spot on turfgrass.

Dollar spot is one of the most common diseases of golf course turfgrass and numerous fungicide applications are often required to provide adequate control. Weather-based disease warning systems have been developed to more accurately time fungicide applications; however, they tend to be ineffective and are not currently in widespread use. The primary objective of this research was to develop a new weather-based disease warning system to more accurately advise fungicide applications to control dollar spot activity across a broad geographic and climactic range. The new dollar spot warning system was developed from data collected at field sites in Madison, WI and Stillwater, OK in 2008 and warning system validation sites were established in Madison, WI, Stillwater, OK, Knoxville, TN, State College, PA, Starkville, MS, and Storrs, CT between 2011 and 2016. A meta-analysis of all site-years was conducted and the most effective warning system for dollar spot development consisted of a five-day moving average of relative humidity and average daily temperature. Using this model the highest effective probability that provided dollar spot control similar to that of a calendar-based program across the numerous sites and years was 20%. Additional analysis found that the 20% spray threshold provided comparable control to the calendar-based program while reducing fungicide usage by up to 30%, though further refinement may be needed as practitioners implement this warning system in a range of environments not tested here. The weather-based dollar spot warning system presented here will likely become an important tool for implementing precision disease management strategies for future turfgrass managers, especially as financial and regulatory pressures increase the need to reduce pesticide usage on golf course turfgrass.

Design of Real-time Disease Warning System Based on Storm Technology

With the improvement of the quality of clinical diagnosis and treatment, the traditional scheduled "ward round" mode cannot meet the demands for real-time monitoring of acute and critically ill patients. This paper introduces the Storm, a real-time data stream processing technology and its application in the real time disease early warning system. By collecting the clinical data flow and calculating the MEWS scores in real time, the system can identify the potential deterioration of the disease, and promptly notify the medical staff. Score calculation results can be stored for further analysis and presentation as well.

Validation of a pedometer algorithm as a tool for evaluation of locomotor behaviour in dairy Mediterranean buffalo.

This research communication validates an algorithm to monitor natural occurrence of locomotor behaviours in dairy Mediterranean buffalo based on the output of a 3-dimensional accelerometer (RumiWatch®, pedometer). Several characteristics of the locomotor behaviour were detected with a very high (up-right, lying and standing time) or high degree of correlation (walking time and number of strides) and a low mean difference with the video recording. The proportion of correctly detected events exceeded 99 % for the following variables: stand up and lie down events, as well as number of lying, standing or walking bouts. The mean relative measurement error was less than 10 % for the variables: lying, standing, up-right times and number of strides as compared with gold standard. This new algorithm may represent the base for a future early and real-time disease warning system aiming to gain higher health standard in these ruminants.

Categorization of apple cultivars based on seasonal powdery mildew disease progression in two disease management systems over 12 years

Five cultivar classification categories were determined from season-long powdery mildew disease progression in integrated and organic production systems. Parameters derived from the progress curve categories were suggested for disease warning improvements. The goal of this 12-year study was to evaluate the powdery mildew infection rates of nine resistant, nine commercial, and nine old apple cultivars in two production systems: integrated and organic. Cultivars were classified into five categories, from low to high, based on their shoot and fruit disease progress curves assessed during the season with the disease progress analysed separately in all five classification categories. Powdery mildew incidence at harvest had increased considerably more for both fruit and shoots in the organic than in the integrated plots, except for the shoots of cultivars ‘Reglindis’, ‘Reka’, ‘Rewena’, ‘Parker Pepin’, and ‘Kings of the Pippin’. Control efficacy was acceptable for all resistant and most old cultivars in both production systems (final disease incidence was below 7.5%), but the level of powdery mildew incidence at harvest was middle-to-high or high for cultivars ‘Idared’, ‘Jonagold’, ‘Jonathan’, ‘Jonica’ and ‘Royal Gala’ from the commercial cultivar group in both production systems. Cultivar categorization for powdery mildew showed that season-long disease progress was low for the old; low-to-middle for the resistant; and middle-to-high for the commercial cultivars. A three-parameter logistic function was fitted to the temporal progress data of each classification category. Then the upper asymptote (Y f), rate parameter (β), and inflection point (M) were estimated from the fitted function and the standardized area under the disease progress curves (AUDPCS) were also calculated. Results demonstrated that AUDPCS and partially Y f were able to differentiate the five mildew classification categories for both shoot and fruit in both production systems. Therefore, AUDPCS and Y f were suggested as input parameters in disease warning systems for measuring host resistance. Yields in the integrated plots were higher compared with those in the organic ones. Powdery mildew classification categories, together with yield, can contribute to a more appropriate cultivar selection for establishing orchards managed by environmentally friendly approaches.

Effect of Inoculum Concentration and Interrupted Wetness Duration on the Development of Anthracnose Fruit Rot of Strawberry.

Anthracnose fruit rot (AFR) of strawberry, caused by Colletotrichum acutatum, greatly affects production if not controlled. Application of fungicides in addition to the use of less susceptible cultivars are important tools for AFR control. The effects of interrupted wetness duration and inoculum concentration on the development of AFR were evaluated on strawberry cultivars with different levels of susceptibility. Fruit rot and flower blight incidence generally increased with increasing inoculum concentration. 'Camarosa' and 'Treasure' were more susceptible than 'Strawberry Festival' and flowers were more susceptible than immature fruit for most cultivars. Interruption in wetness periods had a significant effect on disease incidence when fruit where exposed to dry periods of 6 h or more compared with 24 h of continuous wetness. The results of this study will be used to refine a disease-warning system to predict AFR outbreaks in Florida strawberry production fields.