Predicting factors for malaria re-introduction: an applied model in an elimination setting to prevent malaria outbreaks

Predicting the risk of malaria re-introduction in countries certified malaria-free: a systematic review

BACKGROUND

Predicting the risk of malaria in countries certified malaria-free is crucial for the prevention of re-introduction. This review aimed to identify and describe existing prediction models for malaria re-introduction risk in eliminated settings.

METHODS

A systematic literature search following the PRISMA guidelines was carried out. Studies that developed or validated a malaria risk prediction model in eliminated settings were included. At least two authors independently extracted data using a pre-defined checklist developed by experts in the field. The risk of bias was assessed using both the prediction model risk of bias assessment tool (PROBAST) and the adapted Newcastle-Ottawa Scale (aNOS).

RESULTS

A total 10,075 references were screened and 10 articles describing 11 malaria re-introduction risk prediction models in 6 countries certified malaria free. Three-fifths of the included prediction models were developed for the European region. Identified parameters predicting malaria re-introduction risk included environmental and meteorological, vectorial, population migration, and surveillance and response related factors. Substantial heterogeneity in predictors was observed among the models. All studies were rated at a high risk of bias by PROBAST, mostly because of a lack of internal and external validation of the models. Some studies were rated at a low risk of bias by the aNOS scale.

CONCLUSIONS

Malaria re-introduction risk remains substantial in many countries that have eliminated malaria. Multiple factors were identified which could predict malaria risk in eliminated settings. Although the population movement is well acknowledged as a risk factor associated with the malaria re-introduction risk in eliminated settings, it is not frequently incorporated in the risk prediction models. This review indicated that the proposed models were generally poorly validated. Therefore, future emphasis should be first placed on the validation of existing models.

Receptivity to malaria: meaning and measurement

"Receptivity" to malaria is a construct developed during the Global Malaria Eradication Programme (GMEP) era. It has been defined in varied ways and no consistent, quantitative definition has emerged over the intervening decades. Despite the lack of consistency in defining this construct, the idea that some areas are more likely to sustain malaria transmission than others has remained important in decision-making in malaria control, planning for malaria elimination and guiding activities during the prevention of re-establishment (POR) period. This manuscript examines current advances in methods of measurement. In the context of a decades long decline in global malaria transmission and an increasing number of countries seeking to eliminate malaria, understanding and measuring malaria receptivity has acquired new relevance.

Modelling sociodemographic factors that affect malaria prevalence in Sussundenga, Mozambique: a cross-sectional study

Background: Malaria is still one of the leading causes of mortality and morbidity in Mozambique with little progress in malaria control over the past 20 years. Sussundenga is one of most affected areas. Malaria transmission has a strong association with environmental and sociodemographic factors. The knowledge of sociodemographic factors that affects malaria, may be used to improve the strategic planning for its control. Currently such studies have not been performed in Sussundenga. Thus, the objective of this study is to model the relationship between malaria and sociodemographic factors in Sussundenga, Mozambique.

Methods: Houses in the study area were digitalized and enumerated using Google Earth Pro version 7.3. In this study 100 houses were randomly selected to conduct a community survey of Plasmodiumfalciparum parasite prevalence using rapid diagnostic test (RDT). During the survey, a questionnaire was conducted to assess the sociodemographic factors of the participants. Descriptive statistics were analyzed and backward stepwise logistic regression was performed establishing a relationship between positive cases and the factors. The analysis was carried out using SPSS version 20 package.

Results: The overall P. falciparum prevalence was 31.6%. Half of the malaria positive cases occurred in age group 5 to 14 years. Previous malaria treatment, population density and age group were significant predictors for the model. The model explained 13.5% of the variance in malaria positive cases and sensitivity of the final model was 73.3%.

Conclusion: In this area the highest burden of P. falciparum infection was among those aged 5–14 years old. Malaria infection was related to sociodemographic factors. Targeting malaria control at community level can combat the disease more effectively than waiting for cases at health centers. These finding can be used to guide more effective interventions in this region.

Prevention of re-establishment of malaria: historical perspective and future prospects

Prevention of re-establishment (POR) refers to the prevention of malaria outbreak/epidemic occurrence or preventing re-establishment of indigenous malaria in a malaria-free country. Understanding the effectiveness of the various strategies used for POR is, therefore, of vital importance to countries certified as "malaria-free" or to the countries to be thus certified in the near future. This review is based on extensive review of literature on both the POR strategies and elimination schemes of countries, (i) that have reached malaria-free status (e.g. Armenia, Mauritius, Sri Lanka), (ii) those that are reaching pre-elimination stage (e.g. South Korea), and (iii) countries at the control phase (e.g. India). History has clearly shown that poorly implemented POR programmes can result in deadly consequences (e.g. Sri Lanka); conversely, there are examples of robust POR programmes that have sustained malaria free status that can serve as examples to countries working toward elimination. Countries awaiting malaria elimination status should pre-plan their POR strategies. Malaria-free countries face the risk of resurgence mostly due to imported malaria cases; thus, a robust passenger screening programme and cross border collaborations are crucial in a POR setting. In addition, sustained vigilance, and continued funding for the national anti-malarial campaign programme and for related research is of vital importance for POR. With distinct intrinsic potential for malaria in each country, tailor-made POR programmes are built through continuous and robust epidemiological and entomological surveillance, particularly in countries such as Sri Lanka with increased receptivity and vulnerability for malaria transmission. In summary, across all five countries under scrutiny, common strengths of the POR programmes are (i) a multipronged approach, (ii) strong passive, active, and activated passive case detection, (iii) Indoor residual spraying (IRS), and (iv) health education/awareness programmes.

Fuzzy Multidimensional Model to Cluster Dengue Risk in Sri Lanka

Dengue is the world's rapidly transmitting mosquito-borne viral disease. It is mostly found in subtropical countries in the world. The annual number of global deaths caused by dengue fever is about 25,000. The Sri Lanka dengue situation is also not different to other countries. In the year 2019, dengue fever caused 120 deaths in Sri Lanka. Most of these deaths were reported from the main administrative district Colombo. Health authorities have to pay their attention to control this new situation. Therefore, identifying the hot spots in the country and implementing necessary actions to control the disease is an important task. This study aims to develop a clustering technique to identify the dengue hot spots in Sri Lanka. Suitable risk factors are identified using expert ideas and reviewing available literature. The weights are derived using Chang's extent method. These weights are used to prioritize the factors associated with dengue. Using the geometric mean, the interaction between the triggering variable and other variables is calculated. According to the interaction matrices, five dengue risk clusters are identified. It is found that high population movement in the area plays a dominant role to transmit the disease to other areas. Most of the districts in Sri Lanka will reach to moderate risk cluster in the year 2022.

Influx of diverse, drug resistant and transmissible Plasmodium falciparum into a malaria-free setting in Qatar

BACKGROUND

Successful control programs have impeded local malaria transmission in almost all Gulf Cooperation Council (GCC) countries: Qatar, Bahrain, Kuwait, Oman, the United Arab Emirates (UAE) and Saudi Arabia. Nevertheless, a prodigious influx of imported malaria via migrant workers sustains the threat of local transmission. Here we examine the origin of imported malaria in Qatar, assess genetic diversity and the prevalence of drug resistance genes in imported Plasmodium falciparum, and finally, address the potential for the reintroduction of local transmission.

METHODS

This study examined imported malaria cases reported in Qatar, between 2013 and 2016. We focused on P. falciparum infections and estimated both total parasite and gametocyte density, using qPCR and qRT-PCR, respectively. We also examined ten neutral microsatellites and four genes associated with drug resistance, Pfmrp1, Pfcrt, Pfmdr1, and Pfkelch13, to assess the genetic diversity of imported P. falciparum strains, and the potential for propagating drug resistance genotypes respectively.

RESULTS

The majority of imported malaria cases were P. vivax, while P. falciparum and mixed species infections (P. falciparum / P. vivax) were less frequent. The primary origin of P. vivax infection was the Indian subcontinent, while P. falciparum was mostly presented by African expatriates. Imported P. falciparum strains were highly diverse, carrying multiple genotypes, and infections also presented with early- and late-stage gametocytes. We observed a high prevalence of mutations implicated in drug resistance among these strains, including novel SNPs in Pfkelch13.

CONCLUSIONS

The influx of genetically diverse P. falciparum, with multiple drug resistance markers and a high capacity for gametocyte production, represents a threat for the reestablishment of drug-resistant malaria into GCC countries. This scenario highlights the impact of mass international migration on the reintroduction of malaria to areas with absent or limited local transmission.

Using health facility-based serological surveillance to predict receptive areas at risk of malaria outbreaks in elimination areas

BACKGROUND

In order to improve malaria burden estimates in low transmission settings, more sensitive tools and efficient sampling strategies are required. This study evaluated the use of serological measures from repeated health facility-based cross-sectional surveys to investigate Plasmodium falciparum and Plasmodium vivax transmission dynamics in an area nearing elimination in Indonesia.

METHODS

Quarterly surveys were conducted in eight public health facilities in Kulon Progo District, Indonesia, from May 2017 to April 2018. Demographic data were collected from all clinic patients and their companions, with household coordinates collected using participatory mapping methods. In addition to standard microscopy tests, bead-based serological assays were performed on finger-prick bloodspot samples from 9453 people. Seroconversion rates (SCR, i.e. the proportion of people in the population who are expected to seroconvert per year) were estimated by fitting a simple reversible catalytic model to seroprevalence data. Mixed effects logistic regression was used to examine factors associated with malaria exposure, and spatial analysis was performed to identify areas with clustering of high antibody responses.

RESULTS

Parasite prevalence by microscopy was extremely low (0.06% (95% confidence interval 0.03-0.14, n = 6) and 0 for P. vivax and P. falciparum, respectively). However, spatial analysis of P. vivax antibody responses identified high-risk areas that were subsequently the site of a P. vivax outbreak in August 2017 (62 cases detected through passive and reactive detection systems). These areas overlapped with P. falciparum high-risk areas and were detected in each survey. General low transmission was confirmed by the SCR estimated from a pool of the four surveys in people aged 15 years old and under (0.020 (95% confidence interval 0.017-0.024) and 0.005 (95% confidence interval 0.003-0.008) for P. vivax and P. falciparum, respectively). The SCR estimates in those over 15 years old were 0.066 (95% confidence interval 0.041-0.105) and 0.032 (95% confidence interval 0.015-0.069) for P. vivax and P. falciparum, respectively.

CONCLUSIONS

These findings demonstrate the potential use of health facility-based serological surveillance to better identify and target areas still receptive to malaria in an elimination setting. Further implementation research is needed to enable integration of these methods with existing surveillance systems.

Rainfall Trends and Malaria Occurrences in Limpopo Province, South Africa

This contribution aims to investigate the influence of monthly total rainfall variations on malaria transmission in the Limpopo Province. For this purpose, monthly total rainfall was interpolated from daily rainfall data from weather stations. Annual and seasonal trends, as well as cross-correlation analyses, were performed on time series of monthly total rainfall and monthly malaria cases in five districts of Limpopo Province for the period of 1998 to 2017. The time series analysis indicated that an average of 629.5 mm of rainfall was received over the period of study. The rainfall has an annual variation of about 0.46%. Rainfall amount varied within the five districts, with the northeastern part receiving more rainfall. Spearman's correlation analysis indicated that the total monthly rainfall with one to two months lagged effect is significant in malaria transmission across all the districts. The strongest correlation was noticed in Vhembe (r = 0.54; p-value = <0.001), Mopani (r = 0.53; p-value = <0.001), Waterberg (r = 0.40; p-value =< 0.001), Capricorn (r = 0.37; p-value = <0.001) and lowest in Sekhukhune (r = 0.36; p-value = <0.001). Seasonally, the results indicated that about 68% variation in malaria cases in summer-December, January, and February (DJF)-can be explained by spring-September, October, and November (SON)-rainfall in Vhembe district. Both annual and seasonal analyses indicated that there is variation in the effect of rainfall on malaria across the districts and it is seasonally dependent. Understanding the dynamics of climatic variables annually and seasonally is essential in providing answers to malaria transmission among other factors, particularly with respect to the abrupt spikes of the disease in the province.

A framework for assessing local transmission risk of imported malaria cases

Background

A key issue in achieving and sustaining malaria elimination is the need to prevent local transmission arising from imported cases of malaria. The likelihood of this occurring depends on a range of local factors, and these can be used to allocate resources to contain transmission. Therefore, a risk assessment and management strategy is required to identify risk indexes for malaria transmission when imported cases occur. These risks also need to be quantified and combined to give a weighted risk index score. This can then be used to allocate the resources to each administrative region to prevent transmission according to the degree of risk.

Methods

A list of potential risk indexes were generated from a literature review, expert consultation and panel discussion. These were initially classified into 4 first-level indexes including infection source, transmitting conditions, population vulnerability and control capacity. Each of these was then expanded into more detailed second-level indexes. The Delphi method was then used to obtain expert opinion to review and revise these risk indexes over two consecutive rounds to quantify agreement among experts as to their level of importance. Risk indexes were included in the final Transmission Risk Framework if they achieved a weighted importance score ≥ 4. The Analytic Hierarchy Process was then used to calculate the weight allocated to each of the final risk indexes. This was then used to create an assessment framework that can be used to evaluate local transmission risk in different areas.

Results

Two rounds of Delphi consultation were conducted. Twenty-three experts were used at each round with 100% recovery rate of participant questionnaires. The coordination coefficients (W) for the two rounds of Delphi consultation were 0.341 and 0.423, respectively (P < 0.05). Three first-level indexes and 13 second-level indexes were identified. The Analytic Hierarchy Process was performed to calculate the weight of the indexes. For the first-level indexes, infection source, transmitting conditions, and control capacity, the index weight was 0.5396, 0.2970 and 0.1634 respectively. For the three top second-level indexes, number of imported malaria cases, Anopheles species, and awareness of timely medical visit of patient, the index weight was 0.3382, 0.2475, and 0.1509 respectively.

Conclusions

An indexed system of transmission risk assessment for imported malaria was established using the Delphi method and the Analytic Hierarchy Process. This was assessed to be an objective and practical tool for assessing transmission risk from imported cases of malaria into China.

Electronic supplementary material

The online version of this article (10.1186/s40249-019-0552-6) contains supplementary material, which is available to authorized users.

A social network analysis on immigrants and refugees access to services in the malaria elimination context

Background

There has been significant progress in eliminating malaria in Iran. The aim of this study is to investigate the structure of inter-organizational collaboration networks in the field of unauthorized immigrants and refugees access to services in order to eliminate malaria.

Methods

This study employed social network analysis, in which nodes represented stakeholders associated with providing access of immigrants and refugees to services in the field of malaria elimination, and ties indicated the level of collaboration. This study adopted socio-centric analysis and the whole network was studied. In this regard, 12 districts of the malaria-endemic area in Iran were selected. Participants included 360 individuals (30 representatives of the organization/group in each district). The data were gathered by interview, using the levels of collaboration scale. UCINET 6 was used for data analysis. The indices of density, centralization, reciprocity, and clustering were investigated for each twelve network and at each level of collaboration.

Results

The average density of the networks was 0.22 (SD: 0.04). In districts with a high incidence of imported malaria, the values of network density and centralization were high and the networks comprised of a larger connected component (less isolated clusters). There were significant correlations between density of network (r = 0.66, P = 0.02), degree centralization (r = 0.65, P = 0.02), betweenness centralization (r = 0.76, P = 0.004), and imported malaria cases. In general, the degree centrality and betweenness centrality of the organizations of health, district governor, and foreign immigrants’ affairs were higher. In all networks, 60% of the relationships were bilateral. At a higher level of collaboration, the centralization declined and reciprocity increased. The average of betweenness centralization index was 22.76 (SD = 3.88).

Conclusions

Higher values of network indices in border districts and districts with more cases of imported malaria, in terms of density and centralization measures, can propose the hypothesis that higher preparedness against the issue and centralization of power can enable a better top-down outbreak management, which needs further investigations. Higher centrality of governmental organizations indicates the need for involving private, non-governmental organizations and representatives of immigrant and refugee groups. Recognition of the existing network structure can help the authorities increase access to malaria prevention, diagnosis, and treatment services among immigrants and refugees.

Electronic supplementary material

The online version of this article (10.1186/s12936-018-2635-4) contains supplementary material, which is available to authorized users.

Development of an empirical model to predict malaria outbreaks based on monthly case reports and climate variables in Hefei, China, 1990-2011

Malaria remains a significant public health concern in developing countries. Drivers of malaria transmission vary across different geographical regions. Climatic variables are major risk factor in seasonal and secular patterns of P. vivax malaria transmission along Anhui province. The study aims to forecast malaria outbreaks using empirical model developed in Hefei, China. Data on the monthly numbers of notified malaria cases and climatic factors were obtained for the period of January 1st 1990 to December 31st 2011 from the Hefei CDC and Anhui Institute of Meteorological Sciences, respectively. Two logistic regression models with time series seasonal decomposition were used to explore the impact of climatic and seasonal factors on malaria outbreaks. Sensitivity and specificity statistics were used for evaluating the predictive power. The results showed that relative humidity (OR = 1.171, 95% CI = 1.090-1.257), sunshine (OR = 1.076, 95% CI = 1.043-1.110) and barometric pressure (OR = 1.051, 95% CI = 1.003-1.100) were significantly associated with malaria outbreaks after adjustment for seasonality in Hefei area. The validation analyses indicated the overall agreement of 70.42% (sensitivity: 70.52%; specificity: 70.30%). The research suggested that the empirical model developed based on disease surveillance and climatic conditions may have applications in malaria control and prevention activities.

malERA: An updated research agenda for combination interventions and modelling in malaria elimination and eradication

This paper summarises key advances and priorities since the 2011 presentation of the Malaria Eradication Research Agenda (malERA), with a focus on the combinations of intervention tools and strategies for elimination and their evaluation using modelling approaches. With an increasing number of countries embarking on malaria elimination programmes, national and local decisions to select combinations of tools and deployment strategies directed at malaria elimination must address rapidly changing transmission patterns across diverse geographic areas. However, not all of these approaches can be systematically evaluated in the field. Thus, there is potential for modelling to investigate appropriate 'packages' of combined interventions that include various forms of vector control, case management, surveillance, and population-based approaches for different settings, particularly at lower transmission levels. Modelling can help prioritise which intervention packages should be tested in field studies, suggest which intervention package should be used at a particular level or stratum of transmission intensity, estimate the risk of resurgence when scaling down specific interventions after local transmission is interrupted, and evaluate the risk and impact of parasite drug resistance and vector insecticide resistance. However, modelling intervention package deployment against a heterogeneous transmission background is a challenge. Further validation of malaria models should be pursued through an iterative process, whereby field data collected with the deployment of intervention packages is used to refine models and make them progressively more relevant for assessing and predicting elimination outcomes.