Assessment of methods for prediction of human West Nile virus (WNV) disease from WNV-infected dead birds

<p>Abstract</p> <p>Background</p> <p>West Nile virus (WNV) is currently the leading cause of arboviral-associated encephalitis in the U.S., and can lead to long-term neurologic sequelae. Improvements in dead bird specimen processing time, including the availability of rapid field laboratory tests, allows reassessment of the effectiveness of using WNV-positive birds in forecasting human WNV disease.</p> <p>Methods</p> <p>Using New York State integrated WNV surveillance data from transmissions seasons in 2001–2003, this study determined which factors associated with WNV-positive dead birds are most closely associated with human disease. The study also addressed the 'delay' period between the distribution of the dead bird variable and the distribution of the human cases. In the last step, the study assessed the relative risk of contracting WNV disease for people who lived in counties with a 'signal' value of the predictor variable versus people who lived in counties with no 'signal' value of the predictor variable.</p> <p>Results</p> <p>The variable based on WNV-positive dead birds [(Positive/Tested)*(Population/Area)] was identified as the optimum variable for predicting WNV human disease at a county level. The delay period between distribution of the variable and human cases was determined to be approximately two weeks. For all 3 years combined, the risk of becoming a WNV case for people who lived in 'exposed' counties (those with levels of the positive dead bird variable above the signal value) was about 2 times higher than the risk for people who lived in 'unexposed' counties, but risk varied by year.</p> <p>Conclusion</p> <p>This analysis develops a new variable based on WNV-positive dead birds, [(Positive/Tested)*(Population/Area)] to be assessed in future real-time studies for forecasting the number of human cases in a county. A delay period of approximately two weeks between increases in this variable and the human case onset was identified. Several threshold 'signal' values were assessed and found effective at indicating human case risk, although specific thresholds are likely to vary by region and surveillance system differences.</p

An efficient prediction model for water discharge in Schoharie Creek, NY

Flooding normally occurs during periods of excessive precipitation or thawing in the winter period (ice jam). Flooding is typically accompanied by an increase in river discharge. This paper presents a statistical model for the prediction and explanation of the water discharge time series using an example from the Schoharie Creek, New York (one of the principal tributaries of the Mohawk River). It is developed with a view to wider application in similar water basins. In this study a statistical methodology for the decomposition of the time series is used. The Kolmogorov-Zurbenko filter is used for the decomposition of the hydrological and climatic time series into the seasonal and the long and the short term component. We analyze the time series of the water discharge by using a summer and a winter model. The explanation of the water discharge has been improved up to 81%. The results show that as water discharge increases in the long term then the water table replenishes, and in the seasonal term it depletes. In the short term, the groundwater drops during the winter period, and it rises during the summer period. This methodology can be applied for the prediction of the water discharge at multiple sites

Malaria transmission pattern resilience to climatic variability is mediated by insecticide-treated nets

<p>Abstract</p> <p>Background</p> <p>Malaria is an important public-health problem in the archipelago of Vanuatu and climate has been hypothesized as important influence on transmission risk. Beginning in 1988, a major intervention using insecticide-treated bed nets (ITNs) was implemented in the country in an attempt to reduce <it>Plasmodium </it>transmission. To date, no study has addressed the impact of ITN intervention in Vanuatu, how it may have modified the burden of disease, and whether there were any changes in malaria incidence that might be related to climatic drivers.</p> <p>Methods and findings</p> <p>Monthly time series (January 1983 through December 1999) of confirmed <it>Plasmodium falciparum </it>and <it>Plasmodium vivax </it>infections in the archipelago were analysed. During this 17 year period, malaria dynamics underwent a major regime shift around May 1991, following the introduction of bed nets as a control strategy in the country. By February of 1994 disease incidence from both parasites was reduced by at least 50%, when at most 20% of the population at risk was covered by ITNs. Seasonal cycles, as expected, were strongly correlated with temperature patterns, while inter-annual cycles were associated with changes in precipitation. Following the bed net intervention, the influence of environmental drivers of malaria dynamics was reduced by 30–80% for climatic forces, and 33–54% for other factors. A time lag of about five months was observed for the qualitative change ("regime shift") between the two parasites, the change occurring first for <it>P. falciparum</it>. The latter might be explained by interspecific interactions between the two parasites within the human hosts and their distinct biology, since <it>P. vivax </it>can relapse after a primary infection.</p> <p>Conclusion</p> <p>The Vanuatu ITN programme represents an excellent example of implementing an infectious disease control programme. The distribution was undertaken to cover a large, local proportion (~80%) of people in villages where malaria was present. The successful coverage was possible because of the strategy for distribution of ITNs by prioritizing the free distribution to groups with restricted means for their acquisition, making the access to this resource equitable across the population. These results emphasize the need to implement infectious disease control programmes focusing on the most vulnerable populations.</p

Restoration of time-spatial scales in global temperature data

The objective of this paper is to utilize images of spatial and temporal fluctuations of temperature over the Earth to study the global climate variation. We illustrated that monthly temperature observations from weather stations could be decomposed as components with different time scales based on their spectral distribution. Kolmogorov-Zurbenko (KZ) filters were applied to smooth and interpolate gridded temperature data to construct global maps for long-term (≥ 6 years) trends and El Niño-like (2 to 5 years) movements over the time period of 1893 to 2008. Annual temperature seasonality, latitude and altitude effects have been carefully accounted for to capture meaningful spatio temporal patterns of climate variability. The result revealed striking facts about global temperature anomalies for specific regions. Correlation analysis and the movie of thermal maps for El Niño-like component clearly supported the existence of such climate fluctuations in time and space.This article is available at http://dx.doi.org/10.4236/ajcc.2012.1301