Defines the current state of impact of malaria. positioning the disease as leading cause of death by disease worldwide. In last 12 years, seven million lives have been saved with a reduction of 60% in fatalities.
The presentation draws parallels between malaria and polio eradication and estimates a $2 trillion economic benefit through disease eradication. Malaria No More has established a goal and models for disease eradication.
Methods:
Presentation identifies the Odisha State in eastern coastal India as a region in the country and world with exceptionally high rates of malaria infection. Odisha, which represents 3% of India’s population was reporting 40% of the nation’s malaria case. The interventions in this state are documented to show the effectiveness of the declining malaria burden
Routine surveillance is an important global strategy for malaria control. However, there have been few studies comparing routine indicators of burden, including test positivity rate (TPR) and test-confirmed malaria case rates (CMCR), over spatial and temporal scales.
Objective: To evaluate the relationship between test positivity rate and test-confirmed malaria case rate both in time and space, to provide better understanding of the utility and representativeness of HMIS data for changing malaria burden in endemic settings.
Infectious disease outbreaks, such as the Ebola outbreak in West Africa, highlight the need for surveillance systems to quickly detect outbreaks and provide data to prevent future pandemics. The World Health Organization (WHO) developed the Joint External Evaluation (JEE) tool to conduct country-level assessments of surveillance capacity. However, considering that outbreaks begin and are first detected at the local level, national-level evaluations may fail to identify capacity improvements for outbreak detection. The gaps in local surveillance system processes illuminate a need for investment in on-the-ground surveillance improvements that may be lower cost than traditional surveillance improvement initiatives, such as enhanced training or strengthening data transfer mechanisms before building new laboratory facilities. To explore this premise, we developed a methodology for assessing surveillance systems with special attention to the local level and applied this methodology to the malaria outbreak surveillance system in Mashonaland East, Zimbabwe.
Objective: To conduct a field-based assessment of the malaria outbreak surveillance system in Mashonaland East, Zimbabwe.
In 1911, Christophers developed an early-warning system for malaria epidemics in Punjab based on rainfall, fever-related deaths and wheat prices. Since that initial system, researchers and practitioners have continued to search for determinants of spatial and temporal variability of malaria to improve systems for forecasting disease burden. Malaria thrives in poor tropical and subtropical countries where resources are limited. Accurate disease prediction and early warning of increased disease burden can provide public health and clinical health services with the information needed to implement targeted approaches for malaria control and prevention that make effective use of limited resources. Malaria forecasting models do not typically consider clinical predictors, such as type of antimalarial treatment, in the forecasting models.
Objective
The objective of the research was to identify the most accurate models for forecasting malaria at six different sentinel sites in Uganda, using environmental and clinical data sources.
The Government of Indonesia (GoI) aims to eliminate Malaria by 2030 in 4 stages (Wismarini, 2009. To reach the elimination phase High Case Incidence (HCI) areas go through a pre-elimination phase. The aim of the proposed project is to support one of the Stage 3 provinces in reaching the pre-elimination phase by 2015, and to assist its HCI districts and municipalities in re-orienting their programs to malaria elimination. The pre-elimination phase can be attained by following these evidence-based technical strategies: (1) Prompt & accurate diagnosis of cases; (2) Prompt treatment with effective medicines, including intermittent preventive treatment in pregnancy (IPTp); (3) Selective, targeted & integrated vector control; and, (4) Emergency & epidemic preparedness. (Olumese, 2008).
Objective
To seek collaboration with international research institutions and funding agencies.
Over the past five years, efforts to control malaria have been intensified in Uganda (1). With the intensification of these efforts, accurate and timely data are needed to monitor impact of the interventions and guide malaria control program planning (2, 3). We present data on trends in malaria burden over four years from six outpatient health facilities located in regions of varying malaria endemicity in Uganda.
Objective:
To estimate trends in malaria morbidity at six sentinel sites in Uganda.
Malaria remains a major public health problem in Madagascar. Indoor Residual Spraying (IRS) is the adopted strategy for malaria control in the CHs and Fringe regions of Madagascar. Remotely sensed data analysis combined with Multi-Criteria Evaluation become crucial to target priority areas for intervention.
Objective:
Madagascar is one of the low-income countries with limited resources. In order to minimize the cost of the fight against malaria, the main objective of this study is to identify the priority zone for Indoor Residual Spraying (IRS).
Zanzibar is comprised primarily of two large islands with a population of 1.3 million. Indoor Residual Spraying (IRS) campaigns, distribution of long-lasting insecticide treated bed nets (LLINs), ensuring treatment medication is available, and use of Rapid Diagnostic Tests (RDTs) have reduced Malaria prevalence from 39% in 2005[1] to less than 1% in 2011-2012. This is the third time Zanzibar has been close to eliminating malaria, but there are serious challenges. These include vector resistance to pyrethroids, the shortlived efficacy of LLINs, and resistance to behavior change. Constant traffic with mainland Tanzania and foreign countries also poses the risk of outbreaks. An effective and sustained surveillance and rapid response system is essential to control outbreaks and optimize interventions.
Objective
This presentation aims to share the results of a six-year effort to use mobile health (mHealth) technology to help eliminate malaria from a well-defined geographic area. This presentation will review the history, technology, results, lessons-learned, and applicability to other contexts.
There is growing recognition that an inability to access timely health indicators can hamper both the design and the effective implementation of infectious diseases control interventions. In malaria control, the global use of standard interventions has driven down the burden of disease in many regions. Further gains in high transmission areas and elimination in lower transmission settings, however, will require an enhanced understanding of malaria epidemiology, population characteristics, and efficacy of clinical and public health programs at the local level. Currently, there is a dearth of information available to fine-tune malaria control interventions at the local level. A key obstacle is the fragmentation of data into silos, as existing data cannot be brought together to estimate accurate and timely health metrics.
Objective
Driven by the need to bring malaria surveillance data from different sources together to support evidence-based decision making, we are conducting the “Scalable Data Integration for Disease Surveillance” (SDIDS) project. This project aims to foster the integration of existing surveillance data to support evidence-based decision-making in malaria control and demonstrate a model applicable to other diseases. Central to this initiative is collaboration between academia, governmental and NGO sectors.