Mangaluru has been is classified as a high-risk region for Urban Malaria by NVBDCP [11], endemic for malaria contributing to 85% of malaria cases in the state of Karnataka, India. Being a dual host-disease estmation of RO (reproduction number) is complex, recent mathematical models have been used to estmate RO which ranges from 1 to 3000 (12,13). Efficient participation of multiple stakeholders to manage both hosts determines the results of control measures. Failure to contain malaria over two decades, in spite of the ongoing control programme stipulated a new approach. MCS was introduced in October 2015 to improve ‘surveillance with timeline’and dissemination of case details for appropriate action in the field [9]. Electronic surveillance system helps to connect all stakeholders with necessary information for expected time-bound response in the field to break the transmission chain. A multi-pronged, integrated approach involving all health care providers, time bound field response i.e. active case detection and anti-mosquito measures in geographical area is critical for containment and elimination of malaria thereafter.
IT system for malaria control programme should be user friendly, easy to operate, collect information offline and upload when internet connection is available. MCS is a dedicated IT system which is also integrated to mobile technology, and is designed to be user friendly and easy to operate. However, it required few months to train and implement the available functions of MCS by all the stakeholders namely hospitals and diagnostic centers, field workers and administrators. Over 5 years there was an overall reduction in malaria cases by 83% and monthly incidences reduced to double digits. The trend continued during COVID-19 pandemic when the entire health system was engaged in fighting the dreaded disease. MCS affected most parameters for malaria and contributed to the effective reduction of cases in Mangaluru.
Malariometric Indices: Malariometric indices showed significant changes over 5 years. The incidence of both Plasmodium vivax (Pv) and P. falciparum (Pf) gradually decreased. Initially, ABER increased significantly with predominant contribution from passive surveillance. Stagnation between 2nd and 3rd year after implementing MCS was a consequence of administrative decision to utilize MPW for non-malarial work. In PDY 3, CMETs were formed to supplement the active surveillanceand the results can be seen during PDY 4 and PDY 5. During COVID-19 pandemic, active surveillance could not be carried out efficiently resulting in decreased ABER and increased SPR. However, the incidence of malaria and API continued to decrease without any rebound increase in the ‘post-lockdown’ period. There is a need to have comprehensive approach for malaria elimination since it is a dual host disease with wide ranging RO factor, dormant stage in humans and resistance to various strategies adopted for control or elimination. The ultimate goal of all strategies is to reduce API in the area and reduce the size of malaria map. A dedicated, user friendly system which captures data with timeline will assist in micromanaging multiple strategies.
Reporting of cases: Subsequent to ‘smart surveillance’, behavioural changes with respect to timely reporting of malaria cases were observed among the diagnosticians and it continued through PDY 5. Details of 89% of newly diagnosed cases were uploaded into the system within 48 hrs. Both public and private health care providers reported the malarial cases (Table 3). All these were passive case detection (PCD) from health facilities with exception of reports by ASARC and DVBDCO. Very high rate of passive case detection reflects ‘health seeking behaviour’ of the population and is probably one of the reasons for decrease in incidence even during COVID-19 pandemic. Private sector contribution was higher than public health system and is an indication of definite compliance to non-reporting from private health system which was a major hurdle for malaria control in India [7]. As per WHO, cases of malaria are reported only from public healthcare facilities and hence, a large number of cases are unreported thereby facilitating transmission [14, 15]. However, even where reporting rates in the public health sector are close to a 100% in some countries, more than 50% of malaria patients seek care in the private sector [12]. Hence, reporting from private sector is crucial for malaria control.
Figure 2 indicates average time taken to report from the time of diagnosis. Caprturing the case details and transferring this information to the health workers in the field is the key to initiate control activities. This PCD robust reporting of PCD-initiated active case detection (ACD). Ideal IT system should facilitate robust reporting which is very critical for malaria elimination. It has been observed that early reporting from the diagnosticians continued even during covid epidemic thus resulting in disruption of transmission .
Field response with timeline: Efficient participation of multitiple stakeholders is crucial for effective control measures. Multi-pronged, integrated approach is critical for containment of malaria and elimination thereafter. ‘Smart surveillance’ helps to connect all stakeholders with necessary information for anticipated response in the field to break the chain of transmission. ‘Time-bound’ field response ie active case detection and anti-mosquito measures in the geographical risk areaswere carried out simultaneously. Immediate contact smears and identification of positive cases helps to reduce parasite pool available for transmission. Use of different information systems have been successfully implemented in China. Ease of reporting with timelines, 1-3-7 intiative strategy helped to control malaria in Chinese province. A robust disease surveillance information system was used for this strategy [6, 16].
Surveillance: In the initial year after MCS, an increase in incidence was documented suggesting improved surveillance. In subsequent years, there was a gradual reduction in incidence of malaria. This reduction was not uniform throughtthe year. Although during and immediately after monsoon rains (June to October) there have been variable spikes in incidence, the number of cases gradually reduced during same period year on year (Fig. 1). Surveillance, early case detection, treatment and vector control measures were done as per NVBDCP guidelines with variable results. With introduction of MCS the surveillance was robust time-bound and ‘incidence-centric’. Quick transfer of information from point-of-diagnosis to field workers and surveillance thereafter contributed to 1.8% of reported cases of malaria in the city in PDY 5. Albeit small in number, it is of high significance for breaking transmission. Rapid reporting and information of geolocation have been the strength of malaria control system in Zanzibar for over a decade resulting in low transmission of malarial cases [6,17,18 ]. However, falciparum malaria still remains a problem in Zanzibar and Swaziland [19, 20].
Mosquito control Activities: Use of IT brought about behavioural change among health care providers in the field. A shift from manual documentation to IT system with automation ensured appropriate field response including mosquito control measures. Transmission of malaria depends on Ro which inturn is determined by patient factors (PR or parasite ratio) and mosquito behaviour related to entomological inoculation rate (EIR) [21]. Therefore, it is imperative to prevent transmission of parasite from malaria patient to mosquito. An infected mosquito can continue to transmit sporozoites to many healthy individuals for a longer period. The risk of transmission to surrounding population can be minimized with anti-adult or anti-larval measures in houses around the residence of active malaria cases. Effective source reduction management happened over 5 years with gradual reduction of active breeding habitats (Fig. 4). Measures to reduce breeding and spread are important public health measures in malaria elimination operation [9].
Local Strategies: Eighteen months after digitization an administrative decision was taken to utilize services of MPWs for non-malarial (civic body’s) work resulting in diminishing efficiency in the field. Although the community visits increased by manifold during PDY 3, it was not translated to effective vector control measures and collection of smears by active surveillance reduced from 4.61 per case (PDY 2) to 2.8 per case (PDY 3). This resulted in a slump in the work and non-reduction of malarial incidences during PDY 3. A surge in the number of cases was observed in April-May 2017 which led to increase in malarial indices. To counter this inefficiency, Complete Malaria Elimination Teams (CMETs) were formed at district malaria unit in June 2018. The CMETs conducted Active Surveillance Around Reported Case (ASARC) along with anti-vector activities in the locality. Subsequent to CMETs surveillance reduction of cases was observed in the 4th year. During PDY 5 there had an unprecedented pandemic of COVID-19 and entire nation was under lockdown, public health system was engaged in fighting this new disease. However, the CMETs continued carrying out the visits to malarial houses. This activity is probabaly the mainreason for reduction of malaria during PDY 5.
The Global effort of malaria control is in line with the ‘One World One health’ concept, but then a globally defined ‘one-size-fits-all’ malaria control strategy would be inefficient in endemic areas[22, 23]. Digitization did aid in local modification of strategies. During analysis of new cases, clusters of new cases within a short period of one week, within a defined geographical area were identified (hot-spots) and strategically separate programmes were carried out. One such endeavour was targeted for labourers and daily wage earners. Generally, malaria clinics are open from 9 AM and to 5 PM which were underutilized as it was not convenient for the manual labourers and daily wage earners and low socioeconomic class, as they were engaged in their vocation and income generation activities during that time. Hence, a mobile 24 × 7 clinic using a van and healthcare workers was introduced so that it could visit various places and could also be sent to the site if there was a phone call made to the central malaria helpline number. This helped in not only enhancing the diagnosis, but also treatment and prompt reporting of malaria inmigrant population.
Mappimg and Risk categorization: It has been a long-standing concern for epidimeiologists to quantify and stratify risk for malaria. Risk categorization for strategies and programme management is the key to success of malaria eradication and elimination [21]. MCS captured data on realtimebasis for spatial risk classification. Geographical high-risk categorization is based on API and 43 such wards recorded reduction of incidence by 80% and above over 5 years. Several wards converted from a high API red zone to a lesser API green or yellow zones (Fig. 3). Swaziland adopted Immediate Disease Notification System (IDNS) where toll free number is given to report malaria incidence. Risk prediction model was applied for malaria elimination [6, 20].There is a role to understand geographic trends for planning the strategies at micro-level and further research and review are warranted. Moreover, it may be worthwhile to look at the sociodemographic characteristics of people in these areas as well as the activities like construction and migration or travel [22].
Covid Pandemic and malaria: In PDY5 COVID-19 emergedas major public health challenge and disrupted malaria control programme in general. While Februaury 2020 was mainly focused on preparation to plan strategies to control COVID-19, Nationwide lockdown recorded a derease in number of cases of all diseases as the hospitals were converted to Covid-19 facilities and care centres. Diversion of healthcare workforce towards COVID-19 management, total lockdown of entire country, non-availability of transportation, closure or limited working hours of health facilities hampered anti-malarial activities for a short period of 5 to 6 months. However, Active surveillance (ASARC) continued uninterrupted, routine house visits were less but closure of cases were continued and was fairly good
Accountability: Strenghtening of field work force and capacity building is essential in any public health programme. IT adoption did empower the field workers and it also helped in data-based micromanagement by the administrators as well as field workers. A bidirectional accountability was also observed i.e. from field force to administration and vice-versa. ASARC, time bound action in the geographical area surrounding the new malarial case, continuity in control measures especially during low transmission period (non-monsoon period). The necessity of closing the case on day 14, and its measure reflects functional accountability by field work force. Closure of cases steadily increased and contributed to reduction of malaria incidence. There were delays in closure of cases as a result of multiple factors like non-working days, non-availability of the patient upon visit to home, migration, etc. Nevertheless, over 90% cases were tracked and closed subsequently. An inverse relation between closure and malarial incidence was observed (Fig. 2). Hence, the function of ‘close a case’ acertained complete treatment and parasite clearance therby contributing to transmission control.
Future scope: Five-year data indicates that technology has a major role to play in evaluating epidemiology of malaria as well as malariogenic factors. Learning from MCS application should help to upgrade functions, incorporate analytical and predictive output, warning and alarm systems for compliance in the field. Ideally there is a need to design IT system driven field response for both treatment and vector control analytics and predictions. Since most control measures are similar for all vector borne diseases they be brought under the perviw of independent system to manage vector borne diseases or even infectious diseases.
Malaria elimination by 2030 in India is being envisaged. An excellent information system should be at the core of malaria elimination programmes 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. The operational manual elaborates the strategies. However, these strategies need to be structured with ‘time-bound’ interventions. Figure 6 provides functional description of MCS for good micromanagement which is essential for malaria elimination [5, 6]. All micromanagement data regarding treatment and vector control measures can be quantified in relation to ‘time frames’ for each action. Transmission cycle is effectively broken if field interventions are carried out in the first 7 days of diagnosis. Transmission occurs locally around a reported case, and it is logical to implement effective vector control activity and measure this activity simultaneously. Smart surveillance is able to measure and micromanage control measures for designing local strategies.