Information Systems to Support Surveillance for Malaria Elimination

Robust and responsive surveillance systems are critical for malaria elimination. The ideal information system that supports malaria elimination includes: rapid and complete case reporting, incorporation of related data, such as census or health survey information, central data storage and management, automated and expert data analysis, and customized outputs and feedback that lead to timely and targeted responses. Spatial information enhances such a system, ensuring cases are tracked and mapped over time. Data sharing and coordination across borders are vital and new technologies can improve data speed, accuracy, and quality. Parts of this ideal information system exist and are in use, but have yet to be linked together coherently. Malaria elimination programs should support the implementation and refinement of information systems to support surveillance and response and ensure political and financial commitment to maintain the systems and the human resources needed to run them. National malaria programs should strive to improve the access and utility of these information systems and establish cross-border data sharing mechanisms through the use of standard indicators for malaria surveillance. Ultimately, investment in the information technologies that support a timely and targeted surveillance and response system is essential for malaria elimination.


INTRODUCTION
Robust and responsive information systems are critical for successful malaria control and elimination. [1][2][3][4][5] In elimination settings, surveillance must be an intervention where data collection, analysis, output, and response occur quickly to identify symptomatic and asymptomatic cases, prevent onward transmission, and reduce vectorial capacity. The best way to ensure that this occurs rapidly and efficiently is to work with information systems designed to support malaria surveillance and response. Specifically, in an elimination setting, case reporting needs to shift from being periodic and aggregated at the district or provincial level to real-time reporting of individual geolocated cases (Figure 1).
Technologies that support elimination surveillance can facilitate many essential elements such as real-time or rapid reporting and case and intervention mapping. Here we describe the characteristics of an ideal malaria elimination information system that has the capacity to identify individual cases, analyze and share information, and stimulate real-time action to prevent onward transmission.
This article is one in a series of four that is intended to guide malaria elimination program decision making. These articles draw on both published and unpublished literature and qualitative data gathered from key informant interviews. This article offers specific recommendations to guide the choice of information systems in elimination settings.

METHODS
These findings were informed by published and grey literature. In addition to a review of publications specific to malaria elimination and other disease eradication, the literature search included combinations of the following topics and search terms: chagas, cross-border data sharing, data management, dengue, geospatial, guidelines, influenza, information systems for health, polio, imported malaria, integrated disease surveillance, inter-country collaboration, malaria elimination, monitoring and evaluation, outbreak alert, rapid reporting, sentinel surveillance, surveillance, World health Organization (WHO), yellow fever, zero reporting, and specific country programs mentioned by key informants. A total of 157 documents were identified and reviewed. The authors conducted 21 key informant interviews with malaria field experts, surveillance specialists, geographic information systems (GIS) experts, information technology experts, and members of malaria control and elimination programs, as well as experts in the control and eradication of diseases other than malaria.

CHARACTERISTICS OF AN IDEAL ELIMINATION INFORMATION SYSTEM
Surveillance for malaria control aims to estimate the burden of malaria and inform population-level programs, whereas surveillance for malaria elimination strives to capture and respond to every malaria case. 7 An ideal malaria elimination information system to support surveillance and response activities collects and transmits data about cases and program activities swiftly, incorporates data from other existing surveillance systems in real time and analyzes data to inform rapid response strategies ( Figure 2). 8 To achieve these aims, the system requires several key features as follows: Rapid and complete reporting. Data should be collected from the lowest level and in the most direct manner possible. 9 This includes data collected passively at health facilities and in communities from community health workers as well as through active case detection during case investigations or screening activities and intervention data from district-level malaria programs. Consensus on key indicators, or minimum essential data, that a malaria elimination information system needs to capture is fundamental. Complete, timely reporting is an essential element of a malaria elimination surveillance system. Methods for capturing data must be rapid, locally appropriate, feasible, and sustainable by the malaria program. Malaria should be made a notifiable disease once incidence is low enough that malaria surveillance teams can investigate and report every individual case. Instituting a "zero reporting" *Address correspondence to Colin Ohrt, Malaria Elimination Initiative, Global Health Group, University of California, National Institute for Malariology, Parasitology and Entomology, 35 Trung Van Road, Tu Liem, Hanoi, Vietnam. E-mails: colin.ohrt@ucsf.edu or colin8994@gmail.com policy that requires all reporting sites to communicate the number of cases tested and detected regularly, regardless of whether a new case has been detected. This policy, as outlined in the WHO's surveillance guidelines for poliomyelitis and Japanese encephalitis, will further improve data quality. 10 Zero reporting reduces missing data and helps identify locations where reporting is irregular or incomplete. 11 Incorporation of additional data. Incorporating data sources external to the malaria program, and even the health system, into the malaria elimination information system will improve decision making. For example, the use of census data can provide population denominators, climate and land use data warn of potential areas at risk of outbreaks, and population movement information may indicate the need for a rapid shift in resource targeting. The malaria elimination information system must be flexible enough to receive information from and export to external databases to ensure data can be used by and shared among multiple stakeholders.
Accessible data storage and management. Data should be accessible to key members of the health system from the central level down to the implementation units in the communities. 9,12,13 The database should be appropriate for local conditions, taking into account existing community-level assets and skills, as well as access to power and equipment repair. 14,15 In some locations, a cloud-based database will make the most sense because anyone with Internet access and administrative clearance can submit and obtain data in real time. However, in locations where reliable Internet access is unavailable, local databases that feed into a central server may help ensure that work can continue during Internet outages. Regardless of the type of data storage used, guidelines for timeliness of reporting must be established. 6,16 Data storage and management systems must be computer based and should include a plan for ongoing maintenance. 9 Automated and expert analysis. A malaria elimination information system should include automated data analysis to ensure timely outputs and expert analysis for policy and programming decisions. 9,17 Automation is vital because a malaria elimination information system must receive and analyze data and output results quickly to identify threats such as outbreaks, inform responses, and monitor the functioning of the whole elimination program. It is essential to incorporate technical assistance and capacity building for malaria program staff at all levels to ensure the database is used effectively. Malaria experts working where the surveillance system is implemented, including in the field, should participate in analysis and interpretation of outputs to ensure that recommended interventions are feasible and reflect local conditions. Expert analysis can be used to model the expected impact of different combinations of interventions and has been used in other contexts to improve complex processes and systems and decision making. 18 The models can use data from the surveillance system to help determine which interventions should be used and in what manner to ensure the most impactful, efficient, and cost-effective response. 19,20 The impact of the response can be captured by the surveillance system and can inform further iterative changes to the interventions. Geospatial modeling was conducted in Haiti to produce malaria risk maps as part of an assessment of the feasibility of malaria elimination. 21 Because of the unreliability of passive surveillance data, parasite prevalence data were used to better understand the temporal and spatial distribution of malaria. From this work, optimal interventions and treatment strategies for various populations and locations were suggested.
Customized output and feedback. An ideal malaria information system should automatically generate outputs tailored to the level of the health system that receives them, including visualizations of analyzed data, work task lists, and reports for internal use, external organizations, and donors. Once data are analyzed, visualizing results is essential to effectively share the information. Outputs to the lowest levels should be understandable and directly useful for operational responses, for example, including information that directs the surveillance officer to the household or health facility of the case. Monitoring and evaluation of the outputs are necessary to measure the value added of the malaria elimination information system itself and understand how the system can be improved.
Targeted response. Response to malaria elimination information system outputs needs to be timely, effective, and targeted. 9,13,22 At the local level, once health staff receive outputs, including a work task list, they must take immediate action. 23,24 This list may include households to be screened for infection, receive preventive interventions such as indoor residual spraying with insecticide, and receive health education. As the workers are implementing their task list, they can also collect data that should be uploaded into the information system that will further inform the intervention strategy, such as coverage and use of interventions, and the occupational risk factors of the people within the target area. The uploading of data from the response activity acts to inform the information system that the activity has taken place operating as a tool for monitoring and evaluation. Findings from the review and key informant interviews highlighted that connecting outputs from the information system to action is the weakest element of existing systems.

DESCRIPTION OF EXISTING INFORMATION SYSTEMS
Currently, few malaria information systems exist that can collect, store, analyze, and provide feedback to implementers based on real-time information. Many existing systems are limited in geographic coverage, do not collect sufficient data to inform rapid response, or are not connected to decision making. While no existing malaria information system contains all the elements listed above, below are examples of existing systems that contain elements of what an ideal system might look like and offer valuable lessons on how to conduct surveillance that can lead to effective responses. Comparisons of these and other systems are highlighted in Table 1.
China. The strength of the Chinese Information System for Disease Control and Prevention is its timeliness, ease of reporting, and intuitive 1-3-7 monitoring framework. The 1-3-7 framework dictates that malaria cases be reported within one day, case investigation must occur within three days, and foci investigation and increased prevention measures implemented within 7 days. 6 The recommended responses vary by the levels of endemicity and risk, with "active and passive surveillance, with particular attention to mobile populations," in areas with higher incidence, "passive surveillance in the transmission season and active surveillance targeting transmission foci" in zones with seasonal malaria, and "intensified surveillance and response" in border areas. 34 Solomon Islands and Vanuatu. Automated analyses and customized outputs, as well as the potential to guide targeted, rapid response, are the strengths of the Spatial Decision Support System (SDSS), implemented in Vanuatu and the Solomon Islands. 35 This GIS uses the time and place of malaria cases and intervention coverage to automatically classify areas according to risk and then generate specific response recommendations. The information system creates automated maps of households, including coverage, incidence, and additional geographic and entomologic data. Work task lists are generated for intervention and case management teams for each geo-located house they should visit.    Swaziland. The strengths of Swaziland's malaria information system include rapid case reporting through the Immediate Disease Notification System (IDNS), a surveillance system integrated with the reportable disease system, and surveillance outputs that are rapidly relayed to a team that can initiate a response. 36 The health facility staff members call a toll-free number to report cases to the IDNS, which then sends multiple short message service (SMS) messages with case details to the local malaria program manager and the surveillance team, who investigate within 48 hours. Weekly goals and feedback are provided to surveillance officers to improve coverage and speed of follow-up and screening.
Zanzibar. The strengths of Zanzibar's Malaria Case Notification (MCN) system are its rapid reporting and outputs detailing geo-location of cases. 37 Through MCN, cases are reported in real time and then a tablet-based platform alerts district malaria officers to follow-up, guiding which households are visited to conduct reactive case detection. In this system, surveillance is an intervention, where mobile reporting allows the collection of data in real time that are used to guide a local response. 15,38

LINKAGE BETWEEN REGIONAL AND GLOBAL INFORMATION SYSTEMS
Ideally, national malaria control and elimination information systems would link seamlessly with related regional and global structures, prioritizing cross-border intelligence sharing information regarding transmission hotspots, outbreaks, and human movement. This would lead to appropriate allocation of national and regional resources and timelier targeted action. However, database linkage between countries and within regions is difficult due to the sensitivity of sharing and nonstandardized collection of data. As more countries move toward malaria elimination and cross-border and regional malaria elimination initiatives are implemented, sharing of data should become a priority. In an effort to facilitate data sharing for malaria control, WHO now coordinates a "situation room" that is focused on the 10 African countries with the highest malaria burden, bringing country representatives together virtually every 2 weeks to discuss stock control, funding issues, and to track current and potential outbreaks. Similar regional situation rooms such as the data sharing hub being developed by the Emergency Response to Artemisinin Resistance in the Greater Mekong Subregion could facilitate data sharing and coordination among malaria-eliminating countries.

RECOMMENDATIONS
To build a robust and action-oriented malaria elimination information system, a number of key issues require consideration.
Reporting. In countries pursuing malaria elimination, when incidence is low enough, rapid reporting of cases should be implemented. Once in the malaria elimination phase reporting must be required by law, preferably within a defined period, and appropriately incentivized in all sectors caring for malaria patients, including private sector health providers and militaries. Reporting should include negative diagnostic test results and zero case reporting.
Database management. The malaria elimination database must be manageable by the National Malaria Control Program (NMCP). A malaria elimination surveillance system must provide a framework to guide strategic decision making and support the effective management, coordination, and implementation of interventions. All levels of the malaria control program, from the community to the national level, should receive information from the system. Expert epidemiological and information technology oversight of the system is crucial requiring human resources to support data analysis, including surveillance database managers and epidemiologists who can program database queries, analyze, and interpret data.
Information and results feedback. An effective information system must feed analyzed data back to those executing the malaria program, particularly at the community level. In this review, we found few examples of systems that rapidly shared analyzed surveillance information, which could contribute to more rapid and complete responses. For surveillance to function as an intervention, real-time feedback and effective responses are essential. Global stakeholders must take note of this weakness and target investments to improve appropriate rapid feedback from malaria information systems that lead to effective responses.
Technology. Locally appropriate technologies, such as mobile phones and web-based systems, can help support data quality improvements and reporting timeliness. Most importantly, comprehensive spatial decision support systems that incorporate GIS are invaluable, as they enable mapping of cases and interventions, automated foci identification, and targeted responses. 35,39 Data sharing and commitment. Real-time sharing of standardized malaria data across borders has the potential to contribute to malaria elimination. A key element of malaria elimination programs is rapid and appropriate response to malaria cases. Standardized and streamlined methods and indicators will improve reporting and decision making. Interventions will need to be adapted to the location and population; however, there is an urgent need for generic and adaptable standard operating procedures on which NMCPs can base their surveillance and response strategies. The effective implementation of regional collaborations within malarious regions looking to eliminate may be crucial for the success of national and regional malaria elimination. Currently there are few functioning cross-border malaria elimination collaborations. Ideally, surveillance systems would be unified across countries and would incorporate information from militaries who liaise with government and civilian authorities. Harmonizing existing surveillance systems will require both political and financial commitments in short term and long term. In short-term, commitment is needed to bring stakeholders together to develop political and financial capital for malaria elimination surveillance and information systems. Malaria elimination is a long-term strategy, therefore, commitment is needed to maintain a cadre of workers who can work with the software and adapt it to fit changing circumstances.
Many new technology developments to improve surveillance for malaria elimination appear attractive for investment. However, an investment in technologies is not a panacea; a surveillance system is only as good as its implementation. An excellent information system should be at the core of malaria elimination programs to ensure that all cases are detected and responded to in an effective and timely manner. Investment in robust, response-focused systems is essential to achieve malaria elimination.