Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia

Background: Malaria transmission is complex and is believed to be associated with local climate changes. However, simple attempts to extrapolate malaria incidence rates from averaged regional meteorological conditions have proven unsuccessful. Therefore, the objective of this study was to determine if variations in specific meteorological factors are able to consistently predict P. falciparum malaria incidence at different locations in south Ethiopia. Methods: Retrospective data from 42 locations were collected including P. falciparum malaria incidence for the period of 1998-2007 and meteorological variables such as monthly rainfall (all locations), temperature (17 locations), and relative humidity (three locations). Thirty-five data sets qualified for the analysis. Ljung-Box Q statistics was used for model diagnosis, and R squared or stationary R squared was taken as goodness of fit measure. Time series modelling was carried out using Transfer Function (TF) models and univariate auto-regressive integrated moving average (ARIMA) when there was no significant predictor meteorological variable. Results: Of 35 models, five were discarded because of the significant value of Ljung-Box Q statistics. Past P. falciparum malaria incidence alone (17 locations) or when coupled with meteorological variables (four locations) was able to predict P. falciparum malaria incidence within statistical significance. All seasonal AIRMA orders were from locations at altitudes above 1742 m. Monthly rainfall, minimum and maximum temperature was able to predict incidence at four, five and two locations, respectively. In contrast, relative humidity was not able to predict P. falciparum malaria incidence. The R squared values for the models ranged from 16% to 97%, with the exception of one model which had a negative value. Models with seasonal ARIMA orders were found to perform better. However, the models for predicting P. falciparum malaria incidence varied from location to location, and among lagged effects, data transformation forms, ARIMA and TF orders. Conclusions: This study describes P. falciparum malaria incidence models linked with meteorological data. Variability in the models was principally attributed to regional differences, and a single model was not found that fits all locations. Past P. falciparum malaria incidence appeared to be a superior predictor than meteorology. Future efforts in malaria modelling may benefit from inclusion of non-meteorological factors

Spatial access priority mapping (SAPM) with fishers : a quantitative GIS method for participatory planning

Spatial management tools, such as marine spatial planning and marine protected areas, are playing an increasingly important role in attempts to improve marine management and accommodate conflicting needs. Robust data are needed to inform decisions among different planning options, and early inclusion of stakeholder involvement is widely regarded as vital for success. One of the biggest stakeholder groups, and the most likely to be adversely impacted by spatial restrictions, is the fishing community. In order to take their priorities into account, planners need to understand spatial variation in their perceived value of the sea. Here a readily accessible, novel method for quantitatively mapping fishers’ spatial access priorities is presented. Spatial access priority mapping, or SAPM, uses only basic functions of standard spreadsheet and GIS software. Unlike the use of remote-sensing data, SAPM actively engages fishers in participatory mapping, documenting rather than inferring their priorities. By so doing, SAPM also facilitates the gathering of other useful data, such as local ecological knowledge. The method was tested and validated in Northern Ireland, where over 100 fishers participated in a semi-structured questionnaire and mapping exercise. The response rate was excellent, 97%, demonstrating fishers’ willingness to be involved. The resultant maps are easily accessible and instantly informative, providing a very clear visual indication of which areas are most important for the fishers. The maps also provide quantitative data, which can be used to analyse the relative impact of different management options on the fishing industry and can be incorporated into planning software, such as MARXAN, to ensure that conservation goals can be met at minimum negative impact to the industry. This research shows how spatial access priority mapping can facilitate the early engagement of fishers and the ready incorporation of their priorities into the decision-making process in a transparent, quantitative way

Political fragmentation and land use changes in the Interior Plains

Recent years have witnessed growing interest in the critical role of local/regional governance structures in shaping physical land development and associated natural resource management processes. This article investigates how political fragmentation in local governance can affect land use patterns through a watershed-level analysis of population and employment density changes in the Interior Plains, the largest physiographic division of the US. Population density change rates are found to be negatively associated with a higher degree of political fragmentation, while employment density does not show such a clear relationship with political fragmentation. This finding shows that political fragmentation may present significant challenges to land and water resource management, a result consistent with the previous empirical research

Voluntary disclosure of corporate strategy: determinants and outcomes. An empirical study into the risks and payoffs of communicating corporate strategy.

Business leaders increasingly face pressure from stakeholders to be transparent. There appears however little consensus on the risks and payoffs of disclosing vital information such as corporate strategy. To fill this gap, this study analyzes firm-specific determinants and organisational outcomes of voluntary disclosure of corporate strategy. Stakeholder theory and agency theory help to understand whether companies serve their interest to engage with stakeholders and overcome information asymmetries. I connect these theories and propose a comprehensive approach to measure voluntary disclosure of corporate strategy. Hypotheses from the theoretical framework are empirically tested through panel regression of data on identified determinants and outcomes and of disclosed strategy through annual reports, corporate social responsibility reports, corporate websites and corporate press releases by the 70 largest publicly listed companies in the Netherlands from 2003 through 2008. I found that industry, profitability, dual-listing status, national ranking status and listing age have significant effects on voluntary disclosure of corporate strategy. No significant effects are found for size, leverage and ownership concentration. On outcomes, I found that liquidity of stock and corporate reputation are significantly influenced by voluntary disclosure of corporate strategy. No significant effect is found for volatility of stock. My contributions to theory, methodology and empirics offers a stepping-stone for further research into understanding how companies can use transparency to manage stakeholder relations